## 1 Introduction  
The human brain is the management center and the central organ of the human nervous system, which is responsible for performing daily life activities. The brain receives stimuli or signals from sensory organs of the body, performs processing and sends final decisions and output information to the muscles. Uncontrolled cell division or mutations generate an abnormal group of cells in the brain, resulting in a brain tumor. This type of cell has the ability to affect normal brain function as well as damage healthy cells [1; 2]. Headaches, cognitive issues, vomiting, personality changes, eyesight and speech are some of the most prevalent symptoms of brain tumors. The growth of a brain tumor affects the personality, way of thinking and all other essential functions of patients. Usually, brain tumors are of two types: non-cancerous tumors, which are called benign, and cancerous tumors, which are called malignant. Benign tumors are non-progressive and originate in the brain. This kind of tumor is less aggressive and cannot expand in the body. On the other hand, malignant tumors are cancerous tumors that spread rapidly throughout the body. Furthermore, there are two categories of malignant tumors: primary malignant tumors that originate in the brain and transfer to other body parts, and secondary malignant tumors that initiate in other body regions and spread to the brain [3]. Meningioma, pituitary and glioma tumors are common types of brain tumors. Meningioma arises in the thin membranes, i.e., tissues surrounding the spinal cord and brain. Gliomas arise within the glial cells of the brain. Pituitary tumors grow when cells in the pituitary gland near the brain grow in an abnormal pattern. One of the most life-threatening disorders is a brain tumor. As a result, the timely treatment and identification of brain tumors are required to preserve patients' lives. One possible solution is to use machine learning (ML) algorithms to identify brain tumors and their types in patients automatically. However, because of brain tumors' wide range of sizes, shapes and intensities, classifying them into meningioma, pituitary and glioma tumors is a more difficult task [4]. Moreover, among all brain tumors, meningioma, pituitary and glioma tumors have the highest occurrence rate [5]. Furthermore, brain magnetic resonance imaging (MRI) provides detailed information about the brain's structure due to its high resolution. Hence, MRI images significantly impact automatic medical image analysis [6; 7; 8; 9]. To detect and analyze brain tumors, researchers mostly rely on MRI techniques. Recently, many automated brain tumor detection and classification approaches have been proposed by researchers using MRI images. For the detection of brain tumors, traditional ML algorithms, particularly Multi-Layer Perceptron (MLP) and SVM classifiers, are widely used [10]. DL is a ML subfield in which low-level features aid in the construction of high-level features, producing a hierarchy of features [11]. The DL structure adds more hidden layers between the input and output layers to extend the traditional neural network framework. Researchers are now using DL techniques to solve various medical image analysis challenges, such as image denoising, segmentation, registration and classification [8; 12; 13; 14; 15]. CNNs, which use convolutional filters to accomplish complex tasks, have become the most extensively used DL framework in recent years. Feedforward layers with convolutional filters, pooling layers and fully connected (FC) layers make up the CNN framework. A CNN-based classifier can provide a fully automated classifier considered for brain tumor classification [11]. [PERSON] et al. [16] suggested a CNN-based model to efficiently extract features from brain MRI. Since CNN-based models do not require manual feature extraction, they are faster than standard ML algorithms. However, training a CNN classifier from scratch is difficult and time-consuming and requires an extensive labeled dataset.