# A CNN-Based Strategy to Classify MRI-Based Brain Tumors Using Deep Convolutional Network
## 1 Introduction  
A brain tumor is a mass developed by abnormal cell growth and division inside the skull. Brain tumors are rare and can be fatal [1]. They come in a variety of shapes and sizes and can arise in any place and with varying image intensities [2]. Depending on their origin, brain tumors are classified as primary or metastatic. Primary cancer cells originate in brain tissue [3], whereas metastatic cancer cells become malignant in any other part of the body and spread to the brain [1]. A timely diagnosis of a brain tumor is critical for optimal treatment planning and patient care. Various medical imaging techniques are applied to collect information about tumors. Imperative innovations incorporate Computed Tomography (CT), positron emission tomography (PET), Single-Photon-Emission Computed Tomography (SPECT), Magnetic Resonance Spectroscopy (MRS), and Magnetic Resonance Imaging (MRI). These technologies can be used in conjunction to gather more specific information about tumors. MRI, on the contrary, is the most commonly employed method because of its beneficial properties. MRI is a non-invasive in vivo imaging method that uses radiofrequency waves to trigger target tissues, causing them to form internal images under the influence of a superconducting magnet [4]. The scan delivers hundreds of 2D image slices with good soft tissue contrast in MRI collection, while not using ionizing radiation [5, 6]. During image acquisition, excitation and repetition periods are modulated to create images of varied MRI sequences. The radiologists rely on their training and experiences to manually determine the abnormality of the brain MRI, then categorize them into tumor types [7]. Early detection and classification of brain tumors play a key role in the evaluation case and, consequently,contribute to the selection of the most appropriate treatment to save patients' lives [8]. One of the major difficulties of manual detection is the chance of misclassification of the tumor, which can lead to the wrong treatments for the patients. In addition, as time plays an important role, manual detection has no major advantage here. Therefore, the desire for an automated and quick detection technique is expected [9]. The classification of brain tumors into subgroups is a more difficult scientific problem. The factors causing the problems are brain tumors that vary widely in form, size, and intensity [10], and tumors of different pathological categories that may seem identical [11]. We are keen on classifying abnormal types and normal brain images in this study. The MRI dataset used in this study includes images of the brain without tumors, as well as three different forms of brain tumors. Glioma, meningioma, and pituitary tumors account for approximately 45%, 15%, and 15% of all brain tumors, respectively, in clinical practice [12]. This work is more sophisticated and demanding than conventional binary classification (normal and abnormal), as not only can it identify the problem, it also has the ability to categorize the abnormalities. Machine learning techniques are now frequently utilized in medical imaging [6]. To estimate new topic labels in supervised techniques, an algorithm is used to find a mapping function of input variables and their related output labels. The primary goal is to find intrinsic patterns in training data using techniques, such as Artificial Neural Network (ANN) [13], Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) [14]. Unsupervised learning, on the other hand, is based solely on input variables, as shown by fuzzy c-means [15] and the Self-Organization Map (SOM) [16]. To establish learning, the features of the training images must be extracted, which are typically grayscale, texture, and statistical characteristics. These characteristics are called handmade features and they require the expertise of a specialist with considerable knowledge and the ability to select the most vital aspects. Furthermore, this operation takes a long time and is prone to errors when dealing with large amounts of data [17].