# A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network
## 1 Introduction  
In 2020, it was estimated that 308,102 people were diagnosed with a primary brain or spinal cord tumor in the world [1]. Brain tumors are the 10 th leading cause of death worldwide [2]. It is caused by tissue abnormality that develops within the brain or the central spine. As a result, it disrupts the proper operation of the brain. The causes of brain tumors are unknown; nevertheless, the risk can be enhanced by exposure to radiation and family history [3]. Consequently, detection and identification of brain tumors at an early phase is key to successful treatment. Indeed, it plays an indispensable role in improving treatment and ensuring a higher gain of survival possibility. There are several medical imaging techniques and diagnostic methods used to acquire information about tumors, such as Computed Tomography (CT) scans and Magnetic Resonance Imaging (MRI) scans that can distinguish between normal and abnormal cells that grow in the brain [4]. The medical science field has, in the past few years, seen striking progress leading to accurate classification of brain tumors thanks to AI and deep learning. CNN is used in image processing techniques to segment, identify, and classify MRI images as well as to classify and detect brain tumors. These image processing techniques can be based on the image content analysis described in [5, 6, 7], which plays a dynamic role in various computer vision applications. Recent advances in AI, and in particular inmachine learning and deep learning, have contributed to the development of autonomous objects, such as robots, drones, and cars. This has allowed it to become the most important innovation driving force in the fields of technology and industry. The last few years have been marked by the growing interest in the healthcare sector and diseases detection to enhance the implementations of E-Health services. Deep Learning has recently become an active field of interest that attracts researchers, mostly in the field of medical sciences. It has significantly impacted the study of diseases in numerous ways: in the detection, prediction, and diagnosis of diseases. In [8; 9], the author's proposed solutions and new techniques to impact image reconstruction and recognition performance. Computer science scientists have developed many deep learning algorithms to detect and diagnose diseases such as cancer, lung diseases, diabetes, heart diseases, Alzheimer's disease, hepatitis, liver disease, among others. The attentiveness to deep learning is raised to convolutional neural networks (CNN), a powerful way to learn useful representations mainly of images and other structured data. Convolutional neural networks (CNN) are deep artificial neural networks majorly used in image classification, image segmentation, and objection detection. CNN has shown significant advantages in image recognition [10; 11]. Currently, it is attracting interest in a variety of domains and has achieved a huge advancement in various fields. Recently, new technologies have also taken an interest in other medical fields, such as neurosurgery. In [12; 13], authors showed that Augmented Reality (AR) and mobile devices could help in the operating room. In [14], authors developed a new approach based on deep learning techniques to classify White Blood Cells for disease diagnosing. Experimental results showed that the classification of the modified images is more significant than the classification of the original ones. Authors, in [15; 16], proposed to identify and classify liver diseases by using a deep supervised learning method based on CNN architecture. A classification framework was proposed in [15] and consists of improving the processing images and a segmentation of the liver lesions. In [16], the authors developed a two-step classification approach. The first step is the collection of a sufficient number of isolated training samples. The second step is to train two CNN with the same architecture but employing different optimization algorithms.