Comparative study of performing features applied in CNN architectures Sara LAROUI 1 , Hicham OMARA 2 , Mohamed LAZAAR 3 , Oussama MAHBOUB 4 1,2,4 Abdelmalek Essaadi University, Tetuan – Morocco 3 ENSIAS, Mohammed V University, Rabat, Morocco saralaroui0@gmail.com, Hichamomara@gmail.com, lazaarmd@gmail.com, mahbouboussama@gmail.com Abstract. Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including Medical image analysis. CNN is designed to automatically and adaptively learn spatial hierarchies of features through back-propagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers. This paper presents an approach based on CNN for the classification of brain tumors, based on several characteristics that will be extracted automatically and some performing features that will be used in our CNN, This proposed approach provides efficient results at the level of automatic classification than the other usual methods. Keywords: Deep networks, classification, convolutional neural network, brain tumor, Medical imaging. 1 Introduction With advancements in computer vision (multimedia, computer-assisted editing, computer graphics) CNNs are finding their place in performing better, especially in medical imaging field. This progress is due to considerable work in this area and the availability of international image databases. CNN’s applica tions in medical image analysis date back to the 1990s, when used in a variety of applications ranging from data analysis to decision systems [1]. Actually, by using CNNs the doctors are able to make more accurate and timely decision about patient’s disease, its stage, and diagnosis. We are interested in this article to problem of brain classification from MRI (Magnetic Resonance Imaging) [2]. There are many techniques available for diagnosis of brain tumor from the brain tissues, detection of brain tumor such as conventional radiology, ultrasonography, magnetic resonance imaging, computerized tomography and etc., but the process of diagnosing a number of CT-scan images manually becomes tiresome and also susceptible to error. Therefore, computer aided systems are used to assist the physicians as a second option to reduce the mistakes and errors, this raise the need of the automated com- puterized system, such as CNN [3]– [6]. In literature, there are many applications of CNN in the medical field and has played an important role for automated detection of cancerous cells from mammographic images, Computed topography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. Many researchers has been applied CNN in medical image analysis to detect a brain tumor, such as: Verdhana et al. that were proposed in [3] a low-power architecture for edge detection to detect the bio- medical ICCWCS 2019, April 24-25, Kenitra, Morocco Copyright © 2019 EAI DOI 10.4108/eai.24-4-2019.2284238