Achref Haddaji List3n-Era

Samiha Ayed
email: samiha.ayed@utt.fr

Lamia Chaari Fourati

A Transfer Learning Based Intrusion Detection System for Internet of Vehicles

With the fast expansion of the internet of vehicles (IoV) and the emergence of new types of threats, the traditional machine learning-based intrusion detection systems must be updated to meet the security requirements of the current environment. Recently, deep learning has shown exceptional performance in IoV intrusion detection. However, deep learningbased intrusion detection system (DL-IDS) models are more fixated and dependent on the training dataset. In addition, the behavior changes with the occurrence of attacks. They pose a real problem for the DL-IDS and make their detection more complicate. In this paper, we present a deep transfer learning based intrusion detection in-vehicle (TRLID) model for IoV using the CAN bus protocol. In our proposed model, a data preparation approach is proposed to clean up bus data and convert it to an image for usage as input to the deep learning model. Indeed, we used transfer learning characteristics because they enable us to transfer the source task's knowledge to the target task. Therefore, we trained our model using different dataset including different attacks. The experimental results show that our proposed TRLID achieved good results where the intelligence integration of transfer learning was efficient for attacks detection.

IoV security, Transfer Learning, CAN Bus attacks, Intrusion Detection

I. INTRODUCTION

Recently, the intelligent transportation systems (ITS) in many countries are being gradually expanded to their maximum potential as the number of users continue to increase. The primary goal of these transportation systems are to increase traffic monitoring, road safety and passenger comfort in order to reduce accidents [START_REF] Nundloll | A component-based approach for (Re)-configurable routing in VANETs[END_REF]. VANET, or vehicular ad hoc networks [START_REF] Hasrouny | VANet security challenges and solutions: A survey[END_REF], have been designed as the first ITS to keep the driver informed about real time traffic through exchanging information messages. In VANET two types of communication are possible: vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). The main purpose of VANET is to improve traffic efficiency by decreasing communication time, cost and pollution emissions. However, there are still a number of VANET related emerging issues that need to be solved in existing vehicle networks. Some of these problems are inconsistent internet access, compatibility issues with personal devices, processing power limitations and the lack of cloud computing services, among others. To address these issues, Internet of Vehicles (IoV) [START_REF] Yang | An overview of internet of vehicles[END_REF] which is an emerging system in the ITS were proposed via combining VANET with Internet of things (IoT). The adoption of IoV can effectively enhance smart cities and sustainable energy growth. For instance, the traffic management system in the IoV and autonomous driving vehicle environment could reduce traffic congestion, traffic accidents, and industrial contamination. Intelligent transportation system-networked vehicles can also provide users with a more comprehensive and personalized mobile transportation service. These services include route planning, service recommendation, and intelligent parking, thereby enhancing the transportation efficiency. Mainly Recently, researchers brings machine learning (ML) methods as an applied solution for intrusion detection in vehicular system [START_REF] Al-Jarrah | Intrusion detection systems for intra-vehicle networks: A review[END_REF]. ML based IDS have a stronger capability for processing large amounts of data and detecting unexpected threats. The main concept is to label the network data into normal data or abnormal data by extracting the data features from the network traffic to train a detection model. The initial ML model is applied when a vehicle is created and leaves the factory [START_REF] Li | Transfer learning based intrusion detection scheme for Internet of vehicles[END_REF]. However, when new attacks with new feature distributions have been launched, the model's performance would decrease significantly. To overcome these limitations, transfer learning has been proposed to use data or a model from the source domain to train a machine learning model corresponding to a new task in the target domain. Hence, to fulfill the demands of optimized IoVs intrusion detection systems, in this paper we propose a TRansfer Learning based Intrusion Detection in-vehicle (TRLID) model for IoV using the CAN bus protocol. In addition, We proposed a data preparation method that can effectively clean first the CAN bus data and then transform the data to images to more easily distinguish various cyber-attack patterns. In order to validate and evaluate our method, experiments have been conducted using two in-vehicles datasets, Car-hacking dataset and OTIDS dataset generated from heterogeneous sources that include different types of malicious messages. The experiment results show that the proposed TRLID is efficient for attack detection. The rest of this paper is organized as follows: Section II introduces the related work that uses ML and DL algorithms for vehicle network intrusion detection Section III presents the proposed framework, including data transformation, CNN, transfer learning. Section IV presents and discusses the experimental results. Finally, Section V summarizes the paper.

II. RELATED WORKS

In this section, we present some related studies that work on developing an IDS-based ML for IoV. Many researchers worked on different issues from various perspectives in IoV using multiple technologies for security optimization and attacks detection mechanisms. There are also papers proposing new applied solutions on IoVs, or studying the deployment using ML models. Authors in [START_REF] Yang | Tree-based intelligent intrusion detection system in internet of vehicles[END_REF] proposed an IDS-based solutions using tree-strucutre ML models for both CAN bus and external attacks detection in IoV. They used the Synthetic Minority Oversampling Technique (SMOTE) for data preprocessing to generate additional data for minority classes with few number of data. The authors used the stacking as ensemble learning approach to improve the accuracy. They trained four machine learning models and integrated their outputs into a meta-learner to create a robust classifier. For DL-based solutions, authors in [START_REF] Ullah | HDL-IDS: a hybrid deep learning architecture for intrusion detection in the Internet of Vehicles[END_REF] combined long short term memory (LSTM) and the gated recurrent unit (GRU) models to detect cyberattacks in IoV. The approach is based on different pre-processing methods such as cleaning, shuffling, feature filtering and normalization. These methods are applied to the datasets to improve the performance of the LSTM-GRU model. Meanwhile, authors in [START_REF] Ashraf | Novel deep learning-enabled LSTM autoencoder architecture for discovering anomalous events from intelligent transportation systems[END_REF] designed a specific IDS to identify malicious network activity in In-Vehicle Networks (IVNs), V2V communications and V2I networks using LSTM. The proposed solution consists of three main phases. First, the statistical features are called from both the CAN bus and external network. In the second step, the reduced feature space is given to the recurrent architecture with hidden LSTM layers as sliding temporal windows. After completion of training cycles, minimization of training and validation losses to zero, as well as convergence of weights. It is presumed that the compressed representation of typical traffic has been adequately learned. Finally, the proposed system is evaluated using the car-hacking dataset and the UNSW dataset [START_REF] Lokman | Intrusion detection system for automotive Controller Area Network (CAN) bus system: a review[END_REF]. In addition, authors in [START_REF] Mehedi | Deep transfer learning based intrusion detection system for electric vehicular networks[END_REF] proposed a deep transfer learning based LeCun network (LeNeT) for intrusion detection in the in-vehicle network using the CAN bus protocol. The proposed P-LeNet architecture is made up of seven layers with a total of 12,052 trainable parameters (weights). The layer is the composition of two convolutional layers, two subsampling layers, one flatten layer, one fully connected layer, and one output layer. The proposed approach is divided into two sections: model training and intrusion detection. First, the selected data was pre-processed, and then used to train the model. Then, the most important parameters have been selected for the model. The suggested model was trained with a dataset that was randomly chosen.

III. BACKGROUND

A. Attacks On The CAN Bus For The IoV 1) CAN Frame: All nodes connected to the CAN can receive all packet broadcasts. Additionally, a frame in a CAN packet is specified as a structure; it transports a serie of CAN data (bytes) in the network. The arbitration identifier (ID) field in each CAN frame specifies the priority of the transmitted packets. Indeed, when the ID bit value gets lower, the packet's priority become higher. This protocol is designed to prevent collisions on the CAN bus. The CAN data frame consists of four types of standard CAN frame can be identified : The data frame used for data transfer; The remote frame used to allocate a request to the data frame to be transmitted to the target node; Error frame to notify when an error occurs within a delivered frame, and Overload frame to delay the beginning of the next message when the r has not completed processing the message. The CAN frame structure consists of seven fields, as shown in Figure 1.

• Start of frame (SOF): It is consisting of a single dominant bit and alerts all nodes that transmission has started.

• Arbitration Field: It has 11 bits for identifier and one bit for RTR (Remote Transmission Request). During the arbitration procedure, the identifier is treated as a priority, and the RTR is chosen according to the type of CAN frame.

• Control Field: It provides information for the receiver to determine if all packets were successfully received.

• Data field: It is the data used to transmit information from one node to another. It varies from 0 to 8 bytes.

• CRC Field: It ensures the validity of a message as a cyclic redundancy code (CRC).

• Acknowledge Field: It guarantees that the message was received successfully by the receiver node. If the message is valid, the receiver will notify the sender, and change the recessive ACK bit (logic 1) with a dominant bit (logic 0).

• End of Frame: It denotes that the CAN frame has been terminated by a flag with seven recessive bits.

2) CAN Bus Attacks:

The in-vehicle network attackers are divided into two types: attackers with physical control of nodes and attackers without physical control of nodes. Attackers that have the physical control to the node can physically alter message transmission, to allow a malicious node to broadcast messages in place of a legitimate node. Nodes without physical access to the node inject malicious messages to influence the operation of the vehicle. In this paper, we consider four attacks as follows (Figure 2):

• DoS attack: an attacker may insert messages with a high priority in a cycle of the bus. DoS attack messages use the highest priority identifier, 0x000, to dominate the bus.

• Fuzzy attack: During Fuzzy attack, an attacker can inject random messages with faked identifiers and data. It can analyze the in-vehicle messages and choose target identifiers to create malicious behaviors.

• Spoofing attack : the attacker attempts to inject a message with a certain CAN ID into the CAN bus in order to cause vehicle abnormalities. Unlike Fuzzy attack, Spoofing attack selects the CAN ID normally broadcast on the CAN bus to attack the network, whereas Fuzzy attack can generate any simulated fake ID to initiate an attack.

• Impersonation Attack: An impersonation attacker can stop the message transmission by controlling the target node and manipulating an impersonating node that can transmit data frames and replies to remote frame like targeted node.

B. Fundamentals of Transfer Learning

As previously indicated, in TL, knowledge acquired in the source domain will be transferred to the target domain to enhance learning for the target task. Therefore, we should provide the definitions of a domain and a task in TL.

First, the two component that make up a domain D are the feature space χ and a marginal probability distribution P(X), where X = {x 1 , ..., x n } ∈ χ and n represents the number of feature vectors in X. We note D = {χ, P(X)}.

A task is defined by two parts, a label space L and a decision function f(.). The function f(.) is learned from the feature vector and label space pairs {x i , l i }, where the x i ∈ X and l i ∈ L. Generally, the decision function returns the prediction of label f (x i ) given instance x i . Therefore, the decision function can be written as f (xi) = P (l k |x i )|l k ∈ L, where k = {1, ..., |L|}. For example, in a binary attack detection, L presents the set of all labels of normal data ("0"), and attack ("1"). Based on instance x in the feature space, f (.) predicts the probability of normal data or attack.

IV. PROPOSED SYSTEM

This section presents the overarching workflow of our proposed solutions. We first present the data preparation process, which is the first fundamental step of our system. Then we introduce the established phases of transfer learning.

A. Data Preparation

To reach the best performance and enhance the learning process, the data must be prepared before choosing the TL model. Data preparation involves deleting irrelevant features, converting non-numeric features and removing outliers. To prepare the CAN bus data, we apply two main phases; data cleaning and data transformation.

1) Data Cleaning: The CAN bus dataset is particularly susceptible to inaccuracy and inconsistency. First, Outliers are checking to find out a data point that differs substantially from the rest of the data. In addition, missing data is identified and 2) Data Conversion: The IoV network traffic dataset was recorded in a format other than image, it may be a .csv file, txt, or other type of files. In this section, we discuss the transformation of non-image samples to an image form in order to get effective results for network attack detection. It is essential to transform the CAN signals into images, since the pretrained convolution neural network (CNN) models used for transfer learning are designed to accept 3D image samples as input. we normalize the data collected using:

X new = X -M in(X) M ax(X) -M in(x) * 255 (1)
The network data must be normalized into the range of pixel values of images from the range of 0 to 255. After the normalization phase, a chunk of 27 successive samples is selected to convert the data into image with 9*9*3. where 9 is the number of feature in the two datasets. Each 9 chunks are converted in an image matrix channel. Then the three matrix are mapped into RGB channels of an image. The image label depends on the attack patterns on this image. An image is marked as "Normal" if every sample in the image is a normal sample. In fact, if an image includes attack samples, it will be labeled based on the most common attack in this image. Therefore, we discuss the Figure 3 which displays a broad overview of the process.

B. Transfer Learning

Mainly, transfer learning runs first the ID model generation in the source domain. In the next step, the generated ID model update in the target domain after the knowledge transfer. These two domains are described as follows:

• Source domain: The initial step of the transfer learning based architecture consists of the creation of the source domain intrusion model. A source dataset is used to train and validate the IDS model. We use the CNN as the basic model. The CNN's architecture consists of six convolutional layers, three pooling layers to select the most important features and a dense layer. The output layer involves 5 outputs. Each layer takes the previous layer outputs as inputs for the current layer and performs some non-linearities to transform it into a multivariate series whose dimensions are defined by the number of filters in each layer. The structure of the proposed CNNbased model source is illustrated in figure 4. The first layer is the input layer, which is not considered as a network layer because it does not learn anything. The input layer is designed to take the dataset and pass it to the following layer. The convolutional layers are in charge of executing convolution operations. The rectified linear unit (ReLU) is used with all the convolution layers.

The max-poling layers aid in reducing the amount of computing power required to process the data. They are used to select the most important features. The flatten layer converts the pooled feature map to a single column that is passed to the next layer. The fully-connected dense layer reduces drastically the number of trainable parameters in a deep model while enabling the use of a class activation map which allows an interpretation of the learned features. Finally, the output layer carries a number of neurons that corresponds to the number of classes in the dataset. The softmax function is used as the activation function in this layer to predict a probability distribution between normal and attack scenarios.

• Target domain: The second step of the transfer learning process is to apply the source domain knowledge to the target domain. The CNN convolutional base used for the source domain is frozen to avoid the modification of the weights when the model is retrained, and the classifier is trained with its outputs. The CNN used to train the target domain compromises of the frozen layers of the CNN used in the source domain and fully connected layers as an output layer. The ReLU activation function is used in the hidden layers of FC layers. The output layer consists of a FC network with softmax activation.

V. EXPERIMENTAL RESULTS

A. Experiment Setup

The car-hacking dataset comprises CAN packets collected from the OBD-II port. Each CAN packet is defined by three key features: CAN ID which represents the identifier of CAN packet, DATA[0] to DATA [START_REF] Yang | Tree-based intelligent intrusion detection system in internet of vehicles[END_REF] which represents the 8 data bytes of the packet, and finally the flag which accepts two values, T and R (T: inject packet and R: normal packet). The dataset includes normal traffic as well as three types of attack: (1) DoS attack: DoS packet with CAN ID ="0X000" is injected every 0.3 milliseconds. (2) Fuzzy attack: Random ID and DATA values are injected every 0.5 milliseconds. (3) Spoofing Attack (RPM/gear): It injects certain CAN ID packets relevant to RPM and gear every 1 millisecond. We use the OTIDS [START_REF] Lee | OTIDS: A novel intrusion detection system for in-vehicle network by using remote frame[END_REF] dataset as a target dataset. The OTIDS dataset is also generated by gathering CAN packets via the OBD-II. It includes normal packets as well as DoS attacks with CAN ID of "0X000". The CSV files of fuzzy attacks and impersonation attacks do not indicate if a packet is normal or not. The Fuzzy attack injects faked CAN ID and DATA packets with random values. We extract 9 features from CAN packets : CAN ID and the 8 bytes of the packet DATA[0] to DATA [START_REF] Yang | Tree-based intelligent intrusion detection system in internet of vehicles[END_REF] from the two datasets.

B. System Metrics

We use several metrics including the accuracy, recall, precision, and FSscore, which are respectively determined by equations. We note also:

• T P : represents the number of correctly assigned positive samples.

• T N : represents the number of correctly assigned negative samples.

• F P : represents the number of incorrectly assigned positive samples.

• F N : represents the number of incorrectly assigned negative samples. The accuracy is the average proportion of traffic windows accurately allocated to their corresponding class out of the total number of traffic windows.

Accuracy =

T P * T N T P + F P + T N + F N

The precision is a reliable measurement when the cost of false positives is high. It refers to the usage of advanced technology and methods to pursue high accuracy under the existing physical framework. It is expressed as follows:

P recision = T P T P + F P (3)
The recall is the ratio of the number of class traffic windows that are successfully allocated to the number of class traffic windows.

Recall = T P T P + F N (4)
The F1-score is the weighted average of Precision and Recall, taking both FP and FN into account.

C. Hyperparameter Selection for TRLID Model

The selection of the appropriate hyperparameters is very important to improve the model's performance. In our case, many hyper-parameters need to be tuned and optimized to better fit the CNN model. We use the grid search optimization technique to choose the CNN hyper-parameters. We start by defining a search space which includes number of epochs, batch size, learning rate, and dropout rate and optimizer. For each parameter, the grid search was specified. The inputs for the number of epochs and learning rate parameters are 5, 10, 20, 30 and 0.001, 0.01, 0.1, 0.002 respectively. Additionally, we specified 32, 128, 512 and 1024 for the bath size parameters and 0.2, 0.4, 0.5, and 0.6 for the grid search of drop rate. Adam, SGD, Nadam, and Adamax are chosen as inputs of the optimizer search grid. The optimal grid search optimization hyperparameter values are 30 for epochs, 0.001 for the learning rate, 128 for the batch size, 0.4 for the dropout layer, and Adam for the optimizer.

D. Results and Validation

In this section, we will examine the results of the proposed TRLID model. Our transfer learning model is first trained with the car-hacking dataset. Our CNN-based model were trained using OTIDS dataset. As shown, in figure 5, the data transformation method gives attack images with different feature patterns. The DoS attack samples with the highest priority identifier 0x000 are represented by black patterns. The Gear and RPM have a specific patterns resulted by the injection of messages with certain IDs to masquerade We can also observe that the model had not yet overfitted the training for both datasets. The loss plot indicates that the model performs similarly on both the train and test datasets, and that the decreases as the number of epochs increases. In the training of the source domain model, the accuracy achieved 99.91% and the loss reached 0.002%. While in validation, the accuracy reached 99.97% and the loss reached 0.001%. In the other hand, in the training of the target domain model, the accuracy reached 99.83% and the loss value is 0.001%. Meanwhile, in the validation, the accuracy value is equal to 99.87% and the loss value is 0.001%. These results reflect the efficient performance of our proposed model.

VI. CONCLUSION

The massive quantity of data exchanged between intravehicles posed different challenges to traditional IDS. Recently, new proposed IDS are taking benefit of employing DL. They provide an outstanding performance results and good efficiency. Yet, DL still suffer from different limits such as dependent data and lack of labels or annotations. To address the posed limitations, we proposed a deep transfer learning based intrusion detection in-vehicle (TRLID) model for IoV. Our proposed model is built using CNN on car-hacking dataset and been updated with OTIDS dataset. To validate our proposed model, we established an intense experiment. Indeed, by analyzing the obtained efficient results, we concluded that transfer learning provide an ideal solution for CAN bus attacks detection.

Fig. 1 :Fig. 2 :

 12 Fig. 1: Structure of CAN frame

Fig. 3 :Fig. 4 :

 34 Fig. 3: Data to image transformation

Fig. 5 :Fig. 6 :

 56 Fig. 5: Image transformation for different types of attacks.

  Figure6aand Figure6bdepict the plot curves of the loss and the accuracy, respectively. Since the curves do not constantly rise past a certain point.

  , they

	1 bit	12 bits	6 bits	0 to 8 bytes	16 bits	2 bits	7 bits
	SoF	Arbitration Filed	Control Fild	Data Fild	CRC	ACK	End of Frame
		11 bits	1 bit		1 byte	...	1 byte
		ID	RTR		Data [0]	...	Data [7]


Brahami Menaouer

* Kebir

Nour El-Houda

Detection and Classification of Brain Tumors From MRI Images Using a Deep Convolutional Neural Network Approach

Keywords: Brain Tumor, Deep Learning, Image Processing, Knowledge Management, Medical Decision Support System, Medical Informatics, MRI Image

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Timely disease detection and treatment plans lead to the increased life expectancy of patients. Automated detection and classification of brain tumor are more challenging processes that are based on the clinician's knowledge and experience. For this fact, one of the most practical and important techniques is to use deep learning. Recent progress in the fields of deep learning has helped the clinicians in medical imaging for medical diagnosis of brain tumor. In this paper, the authors present a comparison of deep convolutional neural network models for automatically binary classification query MRI images dataset with the goal of taking precision tools to health professionals based on fined recent versions of DenseNet, Xception, NASNet-A, and VGGNet. The experiments were conducted using an MRI open dataset of 3,762 images. Other performance measures used in the study are the area under precision, recall, and specificity.

INTRoDUCTIoN

With the improvement of modern medical standards, medical imaging technology plays an increasingly important role in research on the medical diagnostic. Similarly, a human brain is centre of the nervous system [START_REF] Choudhury | Brain Tumor Detection and Classification Using Convolutional Neural Network and Deep Neural Network[END_REF][START_REF] Anitha | Development of Computer-Aided Approach for Brain Tumor Detection using Random Forest Classifier[END_REF]. A tumor of brain is a collection of uncontrolled increasing of these cells abnormally found in a different parts of the brain. However, brain tumor has become a key research topic in the medical field. For [START_REF] Mittal | AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction[END_REF], the symptoms of a brain tumors may range from severe headaches and seizures to problems with vision and mental changes, depending on different parts of the body. In fact, early detection of tumor cells plays a major role in the treatment and recovery of patients. Diagnosing a brain tumor usually undergoes a very complicated and time-consuming process. In the medical imaging era, different medical imaging techniques namely X-ray, Magnetic Resonance Imaging (MRIs), Ultrasound, MRS (Magnetic Resonance Spectroscopy), and Computed Tomography (CT), have a great influence on the brain tumor detection and treatment process of patients [START_REF] Jalali | A Study of Classification and Feature Extraction Techniques for Brain Tumor Detection[END_REF][START_REF] Amin | Brain Tumor Detection by Using Stacked Autoencoders in Deep Learning[END_REF]. Furthermore, MRI (Magnetic Resonance Imaging) has demonstrated out as an effective instrument in the location of brain tumor with the assistance of MR Images [START_REF] Kumar | Classification of Brain MRI Tumor Images: A Hybrid Approach[END_REF]. Besides that, Magnetic Resonance Imaging (MRI) is the preferred way to diagnose a brain tumor, as it generates more detailed pictures than Computerized Tomography (CT) scans [START_REF] Sarhan | Detection and Classification of Brain Tumor in MRI Images Using Wavelet Transform and Convolutional Neural Network[END_REF]. MRI images help physicians study and diagnose diseases or tumors present in the brain [START_REF] Ruba | Accurate Classification and Detection of Brain Cancer Cells in MRI and CT Images using Nano Contrast Agents[END_REF]. This technique produces clear and high-quality images in various medical image formats. According to several researchers, a brain tumors can be classified into two types: benign and malignant. Benignant tumors have a homogeneous structure and don't contain disease cells while malign have a heterogeneous structure and contain malignancy cells [START_REF] Jain | Analysis of Brain MRI Tumor Detection and Classification using Hybrid Approach[END_REF].

Meanwhile, automated and accurate classification of MRI, X-ray, and CT brain images is extremely important for medical analysis and interpretation. Over the last few years, numerous techniques are devised for brain tumor classification using different deep learning methodologies considering imaging modalities like MRI, CT, and so on. According to [START_REF] Deepak | Brain Tumor Classification using Deep CNN Features via Transfer Learning[END_REF], the classification of tumors using brain images is a challenging task due to two issues. The first issue is that the brain tumor pose high variations in contrast to size, intensity, and shape. The second issue is that the tumors poses many pathological types, which pose the same manifestation. Moreover, brain tumor classification is an important problem in computer-assisted surgery systems, healthcare artificial intelligence systems in order to give yield more advantages for the medical diagnosis. Apart from this, Deep Learning (DL) has also been able to obtain significant results in the domain of medical diagnosis. Deep Learning has become one of the most common techniques that have achieved better performance in many areas, especially in medical image analysis and classification [START_REF] Al Ayoubi | Diabetic Retinopathy Detection Through Deep Learning Techniques: A Review[END_REF]. According to [START_REF] Ali Khan | Brain Tumor Classification in MRI Image using Convolutional Neural Network[END_REF][START_REF] El Kader Isselmou | Deep Learning Algorithm for Brain Tumor Detection and Analysis Using MR Brain Images[END_REF], because of Deep Learning, significant advancement has been made in medical science like the medical image processing technique which helps doctors to diagnose the disease early and easily. For (Kaur & Singh, 2020), Deep learning (DL) techniques are widely used in the automatic analysis of radiological images. Likewise, Deep Convolutional Neural Networks (DCNNs) has been used in the medical imaging classification and grading since it does not require preprocessing or features extraction before the training process [START_REF] Sasikala | A Wavelet-Based Optimal Texture Feature Set for Classification of Brain Tumors[END_REF]. For (Alqudah et al. 2019), DCNNs are designed to minimize or canceling in sometimes the data pre-processing steps and usually are used to deal with raw images. Deep Convolutional Neural Networks (DCNNs) models is a class of deep, feed-forward artificial neural networks that has successfully been applied to analyzing visual imagery [START_REF] Zabalza | Novel Segmented Stacked Autoencoder for Effective Dimensionality Reduction and Feature Extraction in Hyperspectral Imaging[END_REF]. DCNNs use relatively minimal pre-processing compared to other image classification algorithms.

Motivated by making a contribution to improving the analysis of the medical images, this paper introduces a complete automatic brain tumor detection and classification as fast, accurate and supervised method for clinicians. The remainder of this paper is organized as follows. Section 2 introduces the literature review on deep learning methods and brain tumor detection and classification techniques proposed by various researchers. Section 3 presents the proposed methodology used for automatic detection and classification of brain tumors using deep learning models. The experimental results and discussion are covered in Section 4. Finally, Section 5 concludes and summarizes the key findings and future works.

LITeRATURe ReVIew

Nowadays, medical treatments have witnessed a tremendous development of new advanced systems with high standard new technologies for medical investigation. In the right context, a variety of work recently is done for brain tumor detection and classification. In the medical and health arenas, image classification plays a significant role in medical image processing as medical images have different diversities. For brain tumor classification, they have used MRI and CT scan images. MRI is most vastly used for brain tumor classification. Numerous works have been done on brain tumor classification in recent years. In a previous study, [START_REF] Suganthe | Deep Learning based Brain Tumor Classification using Magnetic Resonance Imaging[END_REF]) have proposed CNN and RNN models for developing a high accuracy system for detecting brain tumor from MRI images. The network that resulted in the highest accuracy during testing has been selected and used as the classifier for brain tumor detection. The proposed work increased the accuracy and reduces the loss when compared to the existing system. Another study by [START_REF] Ramadevi | Recurrent Neural Network (RNN) Analysis for Brain Tumor Classification using Decision Tree Classifiers[END_REF], it is to use deep learning techniques for brain tumor classification. BRAT's dataset is used in this process. Recurrent Neural Network (RNN) is used for features extraction and Decision Tree classifier is used for predictive analysis for precision, accuracy and specificity values of normal and abnormal images in dataset. Yet, [START_REF] Arunnehru | Early Prediction of Brain Tumor Classification Using Convolution Neural Networks[END_REF] have presented an approach to improve tumor detection and classification performance. Initially, the tumor area is clustered with the fuzzy c-means algorithm for discovering the surrounded tumor tissues and also gives important clues for tumor types. Second, the Canny Edge detection applied for the tumor region. Third, the spectral residual for saliency map from the tumor region. In other words, [START_REF] Kalaiselvi | Development of Automatic Glioma Brain Tumor Detection System using Deep Convolutional Neural Networks[END_REF] have developed six convolutional neural networks (CNN) models for finding optimal brain tumor detection system on high-grade glioma and low-grade glioma lesions from voluminous magnetic resonance imaging human brain scans. [START_REF] Li | Brain Tumor Detection Based on Multimodal Information Fusion and Convolutional Neural Network[END_REF]) have proposed a method with three-dimensional MRI brain tumor detection combining multimodal information fusion and CNN is proposed. Moreover, the experimental results show that the three evaluation indexes of dice, SN and SE are optimized respectively, and the two-dimensional brain tumor detection network and the original single-mode brain tumor detection method are compared. A study was conducted by [START_REF] Ucuzal | Classification of Brain Tumor Types by Deep Learning with Convolutional Neural Network on Magnetic Resonance Images using a Developed Web-Based Interface[END_REF]) in order to develop web-based software that can classify brain tumors (glioma, meningioma, pituitary) based on high-precision T1 contrast magnetic resonance images using the convolutional neural network from deep learning algorithm. According to the experimental results, all the calculated performance metrics are higher than 98% for classifying the types of brain tumors on the training dataset. In other words, (Alqudah et al. 2019) have used Convolutional Neural Network (CNN) which is one of the most widely used deep learning architectures for classifying a dataset of 3064 T1 weighted contrast-enhanced brain MR images for grading (classifying) the brain tumors into three classes (Glioma, Meningioma, and Pituitary Tumor). In the work done by (Özyurt et al. 2019), to combine the CNN with neutrosophic expert maximum fuzzy (NS-CNN) sure entropy for brain tumor classification using the neutrosophic set -expert maximum fuzzy-sure method for brain tumor segmentation. They achieved an average success of 95.62%. Likewise, [START_REF] Sajjad | Multi-Grade Brain Tumor Classification using Deep CNN with Extensive Data Augmentation[END_REF] have proposed an extensive data augmentation method fused with VGG-19 CNN architecture for brain tumor classification. The method used for multi-grade classification of brain tumors using segmented brain tumor MRI images. The authors used pretrained VGG-19 CNN for classification using transferee learning and achieved an overall accuracy of values 87.38% and 90.67% for data before and after augmentation respectively. Moreover, [START_REF] Das | Brain Tumor Classification Using Convolutional Neural Network[END_REF]) have proposed a system in order to develop a CNN model for classifying brain tumors in T1-weighted contrast-enhanced MRI images. The system consists of two significant steps. First, preprocess the images using different image processing techniques and then classify the preprocessed image using CNN. Besides that, [START_REF] Siar | Brain Tumor Detection Using Deep Neural Network and Machine Learning Algorithm[END_REF] have used a Convolutional Neural Network (CNN) to detect a tumor through brain Magnetic Resonance Imaging (MRI) images based on the combination of feature extraction techniques with CNN. The accuracy of the Softmax Fully Connected layer used to classify images obtained 98.67%. In [START_REF] Samadi | Change Detection in SAR Images using Deep Belief Network: A New Training Approach based on Morphological Images[END_REF]), a deep learning-based supervised method is introduced to detect synthetic aperture radar (SAR) image changes. This method provided a dataset with appropriate data volume and diversity for training the DBN using input images and the images obtained from applying the morphological operators on them. The detection performance of this method indicates the appropriability of deep learning based algorithms for solving the change detection problems. In an earlier work, [START_REF] Seetha | Brain Tumor Classification Using Convolutional Neural Networks[END_REF] have proposed a deep CNN based system for automated brain tumor detection and grading. The system is based on Fuzzy C-Means (FCM) for brain segmentation and based on these segmented regions a texture and shape features were extracted then these features were fed into SVM and DNN classifiers. The results showed that the system achieved a rate of 97.5% accuracy. [START_REF] Mohsen | Classification using Deep Learning Neural Networks for Brain Tumors[END_REF]) have proposed a system that combines Discrete Wavelet Transform (DWT) features and deep learning (DL) techniques using fuzzy c-mean method for segmenting the brain tumor. The authors was applied DWT to extract the features in order to feed into the principal component analysis (PCA) for feature dimension reduction. The results show that they achieve an accuracy rate of 96.97% and a sensitivity of 97.0%.

Therefore, to overcome the issues associated with the existing models, deep Convolutional Neural Networks models are proposed in this paper to detect and classify brain tumor from MRI images of infected patients.

APPRoACH PRoPoSeD

As per our objective and the motivations, this study is associated with some background ideas and research efforts as shown in Figure 1. Briefly, especially using deep CNN models for detection and classification (Brain Tumor) and supporting it with image processing has been remarkable ideas to follow. As general, widely followed automatic binary classification and detection approach performed with Deep Learning models such as CNN, NASNet-A, Xception, DenseNet, and VGGNet have been directed to the disease of brain tumor (see Figure 1).

Figure 1. Ideas and research efforts on the background of this study

In the context mentioned above, this study followed an easy-to-design image pre-processing and deep learning approach for automatic detection and classify brain tumors, by considering MRI & CT Images as input data. In this respect, Figure . 2 represents the stages within the flow of the introduced deep learning approach. After the image pre-processing based enhancement, the classification was made by using Deep Convolutional Neural Networks (DCNN) models (NASNet-A, Xception, DenseNet, and VGGNet). In next stages, we evaluate our introduced deep learning approach by using 3762 T1-weighted contrast-enhanced MRI images, which are performed as part of patients' routine clinical care. Meanwhile, the image processing technique is important for good image enhancement, which will be effective for better detection and classification at the end. The whole flow is a deep learning approach applied to target image data, which is essential for detection from medical inputs in the form of visual elements (see Figure 2).

MRI Images Datasets

In our work, MRI images used in this study are publicly available image datasets available at Kaggle [START_REF] Badža | Classification of Brain Tumors from MRI Images using a Convolutional Neural Network[END_REF]. The image dataset, provided as a set of slices, used in this study contains 3762 T1-weighted contrast-enhanced MRI images acquired with three kinds of brain tumor, Meningioma, Glioma, and Pituitary tumor [START_REF] Sultan | Multi-Classification of Brain Tumor Images Using Deep Neural Network[END_REF][START_REF] Ismael | Brain Tumor Classification via Statistical Features and Back-Propagation Neural Network[END_REF]. All images were acquired from 285 patients in three planes: Sagittal (1259 images), Axial (1224 images), and Coronal (1279 images) plane. Likewise, the images are different for each patient. The images have an in-plane resolution of 512 × 512 with pixel size 0.49 × 0.49 mm 2 [START_REF] Avşar | Detection and Classification of Brain Tumors from MRI Images Using Faster R-CNN[END_REF]. In Figure 3

Preprocessing

In theory, image processing techniques are increasingly used as a way of diagnosing diseases, including brain tumor diseases. As we all know, data pre-processing is an integral part of deep learning. Moreover, there are many pre-processing techniques, which are applicable in different circumstances. The motivation behind image pre-processing is to improve the quality of visual information of each input image. So some preprocessing is required to remove unwanted noises and to improve the contrast of the MRI images.

In this study, Magnetic Resonance Images (MRI) from the dataset were of different sizes and were provided in int16 format. These images represent the input layer of the network, so they were normalized and resized to 256 × 256 pixels. As per our models, we propose to import the OpenCV-Python package mainly used for image processing. OpenCV contains various tools to solve Computer Vision problems [START_REF] Mahamkali | OpenCV for Computer Vision Applications[END_REF]. It contains low-level image processing functions and high-level algorithms for feature matching namely the Canny Edge Detection (CED) technique which is the most important feature in the application of image processing and computer vision. Canny Edge Detection (CED) is a multi-phase algorithm used to identify the edges of an object in an image [START_REF] Selvakumar | The performance Analysis of Edge Detection Algorithms for Image Processing[END_REF][START_REF] Deng | Image Edge Detection Algorithm based on Improved Canny Operator[END_REF]. In this respect, we cropped the dark edges from the images and took only the brain portion from MRI images by using the Open source Computer Vision (CV) Canny Edge Detection technique.

Deep Convolutional Neural Networks (DCNNs)

Deep Learning (DL) is a part of an artificial neural network technique and a subclass of machine learning. Moreover, DL is part of a broader family of machine learning methods based on learning data representations [START_REF] Brahami | Towards a Model to Improve Boolean Knowledge Mapping by Using Text Mining and Its Applications: Case Study in Healthcare[END_REF]. In DL, multiple layers used for a higher level of the feature from the input dataset. Deep learning methodologies are becoming very common in the fields of medical image classification [START_REF] Aledhari | Optimized CNN-based Diagnosis System to Detect the Pneumonia from Chest Radiographs[END_REF][START_REF] Abdolmanafi | Characterization of Coronary Artery Pathological Formations from OCT Imaging using Deep Learning[END_REF]. Many of the notable and most successful works in medical image classification involved implementing deep learning algorithms such as Convolutional Neural Networks (CNNs). In this field, Convolutional Neural Networks (CNNs or ConvNets), a branch of deep learning, have an impressive record for applications in image analysis and interpretation, including medical imaging [START_REF] Pratt | Convolutional Neural Networks for Diabetic Retinopathy[END_REF]). For [START_REF] Militante | Pneumonia Detection Using Convolutional Neural Networks[END_REF], it is very effective in a multi-layered structure when obtaining and assessing necessary features of graphical images. Furthermore, DCNN is a class of deep, feed-forward artificial neural networks that has successfully been applied to analyzing visual imagery. DCNNs use relatively minimal preprocessing compared to other image classification algorithms [START_REF] Suganthi | Diabetic Retinopathy Classification Using Machine Learning Techniques[END_REF]. According to earlier works, DCNN is a type of deep neural networks that learns features from the input data and uses two dimensional convolutional layers for the processing of two dimensional image data [START_REF] Jyotiyana | Deep Learning and the Future of Biomedical Image Analysis[END_REF]. In general, the deep convolutional neural network is composed of many layers in which many two-dimensional planes of feature mapping form [START_REF] Wang | Pulmonary Image Classification Based on Inception-v3 Transfer Learning Model[END_REF]). According to [START_REF] Abdolmanafi | Characterization of Coronary Artery Pathological Formations from OCT Imaging using Deep Learning[END_REF], the popular network in this DCNN, which has a broad application in medical image analysis is Inception-v3 [START_REF] Szegedy | Going Deeper with Convolutions[END_REF], VGG16 model [START_REF] Litjens | A Survey on Deep Learning in Medical Image Analysis[END_REF]) and ResNet50 [START_REF] Abdolmanafi | Characterization of Coronary Artery Pathological Formations from OCT Imaging using Deep Learning[END_REF]) model is other network of this group. The architecture of CNN models, in any CCN model there are three types of main layer as convolutional layer which, pooling layer and dense layer.

Most of the layers in CNN convert an input image to features. Only the last few layers are used for classification [START_REF] Gadekallu | Deep Neural Networks to Predict Diabetic Retinopathy[END_REF]. Finally, Figure 4 graphically presents the general architecture of a CNN, with its main elements.

In this light, we briefly discuss the parameters, architecture of deep CNNs and sub-networks of the three CNNs models (NASNet-A [START_REF] Zoph | Learning Transferable Architectures for Scalable Image Recognition[END_REF], Xception [START_REF] Chollet | Xception: Deep learning with Depthwise Separable Convolutions[END_REF], DenseNet [START_REF] Huang | Densely Connected Convolutional Networks[END_REF], and VGG16 & 19 [START_REF] Simonyan | Very Deep Convolutional Networks for Large-Scale Image Recognition[END_REF]) for diseases brain tumor detection and classification in order to obtain high accuracy and reduce parameters. As well, the outstanding performance of CNN models inspires us to apply them on medical images.

VGG Network (VGG16 and VGG19) Architecture

The visual geometry group network (VGGNet) is a deep neural network with a multilayered operation. The VGGNet is based on the CNN model [START_REF] Mateen | Fundus Image Classification using VGG-19 Architecture with PCA and SVD[END_REF]. This deep learning method is one of the first attempts at adding depth to improve classification accuracy. The major characteristic of this architecture is instead of having a large number of hyperparameters, and they concentrated on simple 3 × 3 size kernels in convolutional layers and 2 × 2 size in max pooling layers [START_REF] El Asnaoui | Using X-ray Images and Deep Learning for Automated Detection of Coronavirus Disease[END_REF]. During testing, in VGGNet, the test image is directly go through the VGGNet and obtain a class score map. This class score map is spatially averaged to be a fixed-size vector. For [START_REF] Setiawan | Layers Modification of Convolutional Neural Network for Pneumonia Detection[END_REF], VGGNet created the VGG16 [START_REF] Simonyan | Very Deep Convolutional Networks for Large-Scale Image Recognition[END_REF] network architecture with 16 layers and VGG19 [START_REF] Simonyan | Very Deep Convolutional Networks for Large-Scale Image Recognition[END_REF] with 19 layers. According to [START_REF] Hieu | Recognition of Plant Species using Deep Convolutional Feature Extraction[END_REF], VGG16 is a CNN architecture that was used to win the ImageNet ILSVR competition 2014. It is as yet considered as one of the outstanding vision model architecture. Moreover, VGG-19 is useful due to its simplicity as 3×3 convolutional layers are mounted on the top to increase with depth level [START_REF] Saikia | Comparative Assessment of CNN Architectures for Classification of Breast FNAC Images[END_REF][START_REF] Mateen | Fundus Image Classification using VGG-19 Architecture with PCA and SVD[END_REF]. For [START_REF] Zhang | Lung Nodule Diagnosis on 3D Computed Tomography Images Using Deep Convolutional Neural Networks[END_REF], VGG-19 model has roughly 143 million parameters, where the parameters are learned from the ImageNet dataset containing 1.2 million general object images of 1,000 different object categories for training.

The use of uniform and smaller filter sizes on VGG can produce more complex features and lower computing when compared to AlexNet. In summary, we present across the Table 1, the difference between VGG16 and VGG19.

NASNet Network Architecture

In theory, the network architecture for NasNet comes from a Neural Architecture Search (NAS) that uses a reinforcement learning approach to optimize architecture configurations. Moreover, the deep neural network has witnessed growth to the next generation by introducing the concept of optimized network which has been materialized through the concept of NAS by Google ML group [START_REF] Radhika | Performance Analysis of NASNet on Unconstrained Ear Recognition[END_REF]. Besides that, NASNet-A is a scalable CNN architecture that consists of basic building blocks (cells) that are optimized using reinforcement learning [START_REF] Saxen | Face Attribute Detection with MobileNetV2 and NasNet-Mobile[END_REF]. Specifically, several modifications was done based on number of layers, weights, regularization methods, etc., to improve efficiency of the network. According to [START_REF] Cogan | MAPGI: Accurate Identification of Anatomical Landmarks and Diseased Tissue in Gastrointestinal Tract using Deep Learning[END_REF], the NAS uses a controller Recurrent Neural Network (RNN) to find the optimal neural architecture. According to [START_REF] Zoph | Learning Transferable Architectures for Scalable Image Recognition[END_REF], the controller weights are updated with policy gradient. The whole end-to-end setup is shown in Figure 5. In Neural Architecture Search (NAS), a controller RNN predicts architecture (A) from a search space with probability P. A child network with architecture (A) is trained to convergence achieving accuracy (TA). Scale the gradients of P by A to update the RNN controller [START_REF] Cogan | MAPGI: Accurate Identification of Anatomical Landmarks and Diseased Tissue in Gastrointestinal Tract using Deep Learning[END_REF].

As per our study, the model that we use for our experiments is the NasNet-A architecture, which was created based on the results of one of the best searches. Primarily, NASNet-A utilizes depthwiseseparable convolutions, which will be described in Table 2.

Xception Network Architecture

Xception was presented in 2017 by Chollet [START_REF] Chollet | Xception: Deep learning with Depthwise Separable Convolutions[END_REF]. As well, Xception is defined as a hypothesis based on the Inception module, which creates correlations of cross-channels and spatial relations within feature maps of CNN able to be completely decoupled. For [START_REF] Leonardo | Deep Feature-Based Classifiers for Fruit Fly Identification (Diptera: Tephritidae)[END_REF], the Xception architecture is an extension of the Inception architecture which replaces the standard Inception modules with depthwise separable convolutions. The Xception model is 36 layers deep, not counting the fully connected layers in the end. The model contains depthwise separable layers like MobileNet, and it also contains "shortcuts" where the output of certain layers are summed with the output from previous layers. Besides that, the Xception model is robust, stronger than the Inception module, and can operate correlations of cross-channels and spatial relations with maps fully decoupled. According to [START_REF] Jinsakul | Enhancement of Deep Learning in Image Classification Performance Using Xception with the Swish Activation Function for Colorectal Polyp Preliminary Screening[END_REF], after input, data using only one size of 1x1 convolution create separate convolution sizes of 3x3 without average pooling, which proceed in nonoverlapping sections of the output channels to then be fed forward for concatenation (see Figure 6).

DenseNet Network Architecture

In theory, the Dense Convolutional Network (DenseNet) is one of the recently proposed neural network architecture, has achieved state-of-the-art performance in many visual tasks [START_REF] Huang | Densely Connected Convolutional Networks[END_REF]). The DenseNet exploits the condensed network providing easy-to-train and highly parametrically efficient models due to the possibility of feature reuse by different layers [START_REF] Jaiswal | Classification of the COVID-19 Infected Patients using DenseNet201 based Deep Transfer Learning[END_REF]). However, recent extensions of DenseNet with careful expert design, such as 23 Multi-scale DenseNet and CondenseNet [START_REF] Huang | Condensenet: An Efficient DenseNet using Learned Group Convolutions[END_REF], have shown that 24 there exists high redundancy in DenseNet. For [START_REF] Chen | Improved DenseNet with Convolutional Attention Module for Brain Tumor Segmentation[END_REF][START_REF] Chen | Multipath Feature Recalibration DenseNet for Image Classification[END_REF], DenseNet a recently proposed CNN architecture has an interesting connectivity pattern: each layer is connected to all the others within a dense block. According to [START_REF] Li | Classification of Breast Cancer Histopathological Images Using Interleaved DenseNet with SENet (IDSNet)[END_REF], the Dense Block is an important part of the DenseNet for improving the information flow between layers. It is composed of BN, ReLu, and 3 × 3 Conv (see Figure 7).

eXPeRIMeNTAL ReSULTS

In the literature, most of the research works that apply deep CNN models for brain tumor classification use hundreds of thousands of images to train the model. In this section, we presents the results of the performance on the MRI & CT scan images dataset.

System Requirements

The experimental environment of this paper is Windows 10 system, Python 3.6.2, Tensorflow 1.11.0, and Keras 2.2.4. In the hardware device section, the CPU is Intel Core i7 4300U @ 1.90 GHz 2.50 GHz specifications, GPU 1060 6 Gb D5 amp, Solid State Drive, Double Data Rate4 32 Gb, and MSI Z270 GAMING PRO CARBON Motherboard. The emulator is written in Python and uses the neural network library Keras and GPU.

evaluation Criteria

The performance of a brain tumor classification approach is evaluated by various performance metrics. The accuracy metric, which determines the correctness of the identified instances in both classes of binary classification (Tumor (T) and No Tumor (NT)), must be supplemented by other metrics such as precision, recall, F1 score, and AUC. These popular parameters are defined as follows:

Sensitivity Recall TP TP FN ( ) = + / (1)
Sensitivity (true positive fraction) is the probability that a detection test is positive, given that the person has the tumor disease. The recall (Specificity) (true negative fraction) is the probability that a detection test is negative, given that the person does not have the disease:

Recall Specificity TN TN FP ( ) = + / (2)
In general, sensitivity and specificity evaluates the effectiveness of the algorithm on a single class, positive and negative respectively:

Accuracy TP TN TP TN FP FN = + + + + /
(3)

Precision TP TP FP = + / (4)
The precision metric will show the ratio of true positives over the total number of detected entities. In other words, this metric will help us understand how well a model is in returning only the true positives and not unrelated entities:

F score Precision Recall Precision Recall - = + 2 * * (5)
Commonly, accuracy is the most used metric to evaluate the classification performance. This metric calculates the percentage of samples that are correctly classified. As well, precision is how "precise" the model is out of those predicted positive and how many of them are actually positive. A high value of the metric (F-score) indicates that the model performs better on the positive class. Thus, F1 Score (also known as F-measure) might be a better measure when a balance between Precision and Recall is needed with an uneven class distribution (large number of Actual Negatives). This metric can be used to show the overall performance of a tool.

Likewise, a confusion matrix is commonly used to visualize the performance of a classification algorithm. Measurement of TP, FP, TN, and FN uses a confusion matrix of a classification with two classes. where TP, TN, FP, and FN stand for true positives, true negatives, false positives, and false negatives, respectively.

Results and Discussions

In this study, the most widely used MRI images dataset has been chosen to verify the proposed approach using Python programming language with Tensorflow framework. For this, we investigated the automatic binary classification of the cases of brain tumor disease using deeper and dense networks (Baseline CNN, Fine-tuning the top layers of DenseNet, Xception, NASNet-A, VGG16, and VGG19). This method can perform classification based on the various status of the T1-weighted contrast-enhanced MRI images (see Section 3.1). Multiple layered models have been designed for performing convolution and feature extraction. The Rectified Linear Unit (ReLU) activation function is used to define the output of internal layers. Moreover, the training curve is calculated from the training dataset that provides an idea of how well the model is learning. Similarly, the validation curve or test curve is calculated from a hold-out validation dataset that provides an idea of how well the model is generalizing. In theory, losses are the errors that occurred in the process of prediction while training the model. The optimum training process always reduces errors and increases accuracy. As per our approach, the lower the loss better is the model and the higher the accuracy more satisfactory is the classification results. During the training process, the approach determines the graphs for the model's loss and accuracy for 70 epochs.

• Baseline CNN: The graphical representation of training loss vs validation loss and training accuracy vs validation accuracy of baseline CNN model is displayed from Figure 8.

According to the figure above, there is an optimal convergence between the training data and the validation data in the accuracy model. As well, we are correctly monitoring the increase in precision and loss between train and validation in epoch this reflects that with each epoch the model is learning more information. For the loss model, the accuracy and loss start to deviate steadily, this could be a sign of stopping training at an earlier time. Likewise, we see that the global confusion matrix illustrated in Figure 8 giving a satisfactory result for the two classes such that the totality of the images well classified in the two classes "No Tumor" and "Tumor" is 361 and 264 with a precision of 0.94 and 0.86 respectively. Moreover, the totality of misclassified images is 82 images out of 706 test images with an accuracy curve of test data with 95.72%. • VGG16: The graphical representation shown in Figure 9 gives the percentage values of performance measures obtained for the VGG16 Model in a binary classification scenario.

The results are given by VGG16 (see Figure 9) show that the Tumor class was detected with good sensitivity of 94.03% that is caused by the low sum of false negatives with reasonable precision of 95.54%. As it is observed, the accuracy value is 95.58%. From these graphs, we notice that the training and validation precision increases with the number of epochs. Likewise, the training and validation error decreases with the number of epochs. From the confusion matrix, we can observe for image class "No Tumor" the model was able to predict 365 images correctly in the no tumor class, but 18 were labeled as Tumor with an error rate of 9.04%. For the image's class "Tumor", the model was able to identify 293 images correctly, but 30 images were labeled as "No Tumor" with an error rate of 9.04%. • VGG19: As it is shown in the figure below (see Figure 10), it is observable that the VGG19 model provided an average accuracy of 96.27% that increases with the number of epochs in the curve of training data (validation data), this reflects that with each epoch the model learns more information. Likewise, we notice that the total misclassified images are 59 images, with an error rate of 4.46%, and the total well-classified images are 647 an accuracy rate of 96.27%. From the confusion matrix, we can observe for the image class "Tumor" the model was able to predict 299 images correctly in the tumor class, but 35 were labeled as no tumor. For the image's class "No Tumor", the model was able to identify 348 images correctly, but 24 images were labeled as Tumor.

• DenseNet: Concerning the DenseNet model results (see Figure 11), we may notice that the Tumor class was detected with good precision, sensitivity, and specificity (95.55%, 95.51%, and 95.25% respectively). This can be explained by the sum of false positives and false negatives were low (precision and sensitivity). It also provides an accuracy rate of 95.53% and a loss of 19.62%. The confusion matrix indicates that, for images class "Tumor", 285 images were predicted correctly model was able to predict 358 images correctly in the "No tumor" class, but 25 were labeled as "Tumor". For the image's class "Tumor", the model was able to identify 294 images correctly, but 29 images were labeled as "No Tumor".

As per our objective, we investigated in this paper the binary classification (No Tumor and Tumor) based on MRI & CT scan images using deep convolutional neural networks architectures, in order to identify the best performing architecture based on the various performance metrics defined in evaluation criteria (see Section 4.2). Precision, sensitivity, and specificity are the key metrics for checking the accuracy of a model. Moreover, we evaluate the F1-score, which checks the accuracy of the test data in the form of harmonic average specifically for imbalance MRI & CT scan images dataset. Table 3 illustrates a comparison between our different deep learning models used in our experiment in terms of popular parameters.

Furthermore, based on Table 3, it can be seen that the table depicts in detail classification performances across multi-experiment classification, based on different fine-tuned versions of recent deep learning architectures. From the results, it's notable that the accuracy when we use the baseline Xception is low compared with other DL architectures since these last models help to obtain respectively 94.93% of accuracy. In contrast, the highest accuracies are reported by CNN, VGGNet, NASNet-A, and DenseNet. Therefore, these models attain better classification accuracy since they respectively achieve 96.65%, 96.27%, 95.72%, 95.58%, and 95.53%.

Besides that, we conducted a comparative study of our proposed approach with other existing brain tumor detection models on grounds of the approach used, a number of MRI & CT Scan images 4). Due to the importance of the diagnosis given by the physician, we developed a platform for physicians to help in diagnosing the Brain Tumor (see Figure 14). Hence, timely and prompt disease detection and treatment plan leads to improved quality of life and increased life expectancy in these patients. By this, the physician will find it friendly to use. He can just log in and can enjoy the services with fast processing and immediate results with high security.

CoNCLUSIoN

The medical field is the most sensitive of all the domains ever known, for the simple reason that it deals with humans. Likewise, deep learning was widely applied to several applications and proven to be a powerful machine learning tool for many complex problems as medical imaging. At the present time, detection of brain tumors from MRI & CT scan images is of vital importance for both doctors and patients to decrease the detection time and reduce financial costs. This paper presents an automated approach used to classify the T1-weighted contrast-enhanced MRI images into brain tumor and the no tumor class using five deep learning architectures (a baseline CNN, VGG16, VGG19, DenseNet, Xception, and NASNet-A). In the right context, the experiments were conducted using MRI images open dataset which contains 3,762 images acquired with three kinds of brain tumor, Meningioma, Glioma, and Pituitary tumor. All images were acquired from 285 patients in three planes: Sagittal (1259 images), Axial (1224 images), and Coronal (1279 images) plane, and performances were evaluated using various performance metrics. Likewise, the obtained results show that the CNN, VGGNet, NASNet-A, and DenseNet gave high performance (accuracy is more than 96%) against other architectures cited in this study (accuracy is around 94%). This automated approach can perform binary classification without manual feature extraction with an accuracy of 96.65%. Due to the high achieved by this model, we believe that these results help medical experts to make decisions pertinent. Moreover, our approach has been implemented as a simple supportive tool for medical doctors in the detection and classifying of brain tumors and in screening tumor patients in medical oncology services.

For future works, we aim to develop a full system for tumors via deep learning detection, segmentation, and classification with tumor research labs. In addition, the results may be improved using more datasets (big data) that can be added to improve the model performance, more experiments on hyperparameters settings in order to improve the approach, and more sophisticated feature extraction techniques based on deep learning that was developed for the biomedical image. As well, we aim to improve our approach for categorical classification problems such as identification of brain tumor types such as Glioma, Meningioma, and Pituitary or may be used to detect other brain abnormalities. Finally, we plan to improve the result visualization using class mind mapping [START_REF] Brahami | An Approach to Dynamic Fusion of the Knowledge Maps of an Activities Process: Application on Healthcare[END_REF] in order to give a better understanding of the result.

  , three different MRI images are presented along with their tumor region. The tumors are marked with a red outline. As per our objective, this dataset (3,762 MRI images) has been used for analyzing the performance of deep learning algorithms used for automated detection and classification of brain tumors. The dataset is organized into three folders (Training, Testing, and Validation) and contains subfolders for each image category (Tumor (T) and No Tumor (NT)). To do that, each training set contains 2315 images, each test set contains 706 images, and each validation set contains 706 images.

Figure 2 .

 2 Figure 2. Main Steps of the proposed hybrid approach

Figure 4 .

 4 Figure 4. General Architecture of a CNN

Figure

  Figure 5. NAS network architecture search

Figure 6 .

 6 Figure 6. Xception Module Architecture

Figure

  Figure 7. DenseNet Architecture

Figure 8 .

 8 Figure 8. Result of Baseline CNN Model (Accuracy, loss curve and confusion matrix 'Actual and predicted labels are displayed on the y-axis and x-axis, respectively")

Figure 9 .

 9 Figure 9. Result of VGG16 Model (Accuracy, loss curve and confusion matrix 'Actual and predicted labels are displayed on the y-axis and x-axis, respectively")

Figure 10 .

 10 Figure 10. Result of VGG19 Model (Accuracy, loss curve and confusion matrix 'Actual and predicted labels are displayed on the y-axis and x-axis, respectively")

Figure 11 .

 11 Figure 11. Result of DenseNet Model (Accuracy, loss curve and confusion matrix 'Actual and predicted labels are displayed on the y-axis and x-axis, respectively")

Figure 12 .

 12 Figure 12. Result of Xception Model (Accuracy, loss curve and confusion matrix 'Actual and predicted labels are displayed on the y-axis and x-axis, respectively")

Figure 13 .

 13 Figure 13. Result of NASNet-A Model (Accuracy, loss curve and confusion matrix 'Actual and predicted labels are displayed on the y-axis and x-axis, respectively")

Table 1 . Comparison of VGG16 and VGG19 Layers

 1

	Layer	VGG16	VGG19
	Size of Layer	41	47
	Image Input Size	244*244 pixel	224*244 pixel
	Convolutional Layer	13	16
	Filter Size	64 & 128	64,128,256, & 512
	ReLU	5	18
	Max Pooling	5	5
	FCL	3	3
	Drop Out	0.5	0.5
	Softmax	1	1

Table 3 . Illustrates a report of evaluation metrics

 3

		Precision	Recall	F1-score	Specificity	Accuracy	Loss	ROC-
			(Sensitivity)					AUC
								Score
	CNN	95.60%	94.26%	94.43%	94.01%	95.72%	19.77%	95.61%
	VGG19	96.18%	95.33%	95.51%	95.26%	96.27%	11.66%	96.16%
	VGG16	95.54%	94.03%	94.01%	93.78%	95.58%	14.14%	95.94%
	DenseNet	95.55%	95.51%	95.62%	95.25%	95.53%	19.62%	96.03%
	NASNet-A	96.63%	95.75%	95.63%	96.11%	96.65%	11.53%	97.89%
	Xception	96.65%	95.86%	95.83%	96.90%	94.93%	20.45%	95.02%

Table 4 . Comparison of proposed brain tumor detection and classification approach with existing classification methodologies

 4

	Author	Dataset	Methodology	Features	Accuracy
	Paul et al. 2017	MRI (CE-MRI)	Fully connected and	Deep Features Extraction	91.43%
		images (Brain	CNN structure	and Classification
		Tumor)
	Mohsen et al. 2018 MRI images & CT	Deep Learning	Discrete Wavelet Transform	96.27%
		Scan images (Brain	Techniques	(DWT) features
		Tumor)
	Abiwinanda et al.	T-1 Weighted CE-	CNN Technique	Model Based	84.19%
	2018	MRI images
	Sajjad et al. 2019	MRI images (Brain	VGG-19 CNN	Model Based	90.67%
		Tumor)	Architecture
	Sultan et al. 2019	MRI images (Brain	CNN Architecture for	Deep Features Extraction	96.13%
		Tumor)	multi-classification	and Classification
	Suganthe et al.	MRI images (Brain	CNN and RNN models	Deep Features Extraction	90.00%
	2020	Tumor)		and Classification
	Jaiswal et al. 2020	CT Scan images of	Deep transfer learning	Features Extraction	96.25%
		COVID-19	models (DTL) with
			DenseNet201
	Proposed Approach MRI and CT Scan	Deep CNNs Models	Deep Features Extraction	96.65%
		images (Brain	(VGGNet, Xception and	and Classification
		Tumor)	NASNet

Figure 14. Brain tumor medical analysis application

Figure 3. Representation of MRI images showing different types of tumors in different planes. In the images, the tumor is marked with a red outline.