## 1 Introduction  
Over 350,000 people worldwide will have a primary brain tumor in 2021. According to World Health Organization (WHO) statistics, brain tumors are the tenth most common cause of death [1]. Brain tumors are unstable and may be harmful if not treated wisely. Tumors often begin to grow in the brain, likely spreading to other body regions over time. These tumors can change the structure of the brain. Brain damage can have a significant impact on the functionality of the body. Unrestricted and aberrant cell growth inside or around the brain is the cause of brain tumors. These tumors, which are categorized according to cell type and growth site as meningiomas, gliomas, and pituitary tumors, represent serious health hazards [2]. Figure 1 indicates the main types of brain tumors, i.e., meningiomas, gliomas, and pituitary tumors. Meningioma and glioma are deadly brain tumors if they are not diagnosed in their early stages. Patients typically do not survive for more than a year [3]. Therefore, early brain tumor detection is essential for carefully formulating treatment plans. Early BT diagnosis may increase the patient's probability of survival and help the medical practitioners to improve and plan suitable treatment utilizing MRI. MRI and computer tomography (CT) are frequently employed in finding and evaluating brain tumors. These methods help medical practitioners identify the presence and location of indicators by thoroughly assessing information about the body's inside. MRIs produce a detailed image of the body using magnetic fields. It is the most widely used diagnostic tool among many imaging technologies for detecting various kinds of tumors. MRI is a painless medical imaging technique that produces high-quality pictures that provide 2D and 3D details images of brain tumors in different sizes [4]. However, reviewing these images manually is time-consuming and prone to mistakes. Furthermore, tumors can take on a variety of shapes, which further complicates the process of making an accurate diagnosis [5]. The development of efficient and computer-based procedures that are both automated and effective is the solution to this problem. This technique ought to result in cost savings. Deep learning and artificial intelligence (AI) have witnessed significant advancements in recent years, which have resulted in extraordinary progress in the field of medical research, notably in the accurate identification and categorization of brain tumors [6; 7]. Recently, deep learning has garnered a lot of attention, particularly from researchers who are particularly interested in medicine. It has a substantial impact on a number of different aspects of illness research, including diagnosis [8], prognosis, and detection [9]. These approaches usually rely on picture content analysis, which is a key component of a wide variety of computer vision applications and is continuously undergoing technological advancement. Consequently, the advancement in deep learning and Internet of Things (IoT) in health sector are today considered to be the primary driving forces behind the development of health industry [10; 11]. It has been noticed that the healthcare business is showing a growing interest in the identification of illnesses, with a special emphasis on the enhancement of the implementation of E-Health services [12]. The neurosurgical perspective is mainly used to diagnose the three forms of brain tumors early [13]. Different ML/DL models for brain tumor detection have been carried out, particularly the transfer learning model [14]. In the classification and scaling of medical imaging, the CNN model has expanded substantially in support of transfer learning. It is repeatedly used to interface with raw images and improve classification. In addition, the attainment of its features is dependent on a transfer learning model such as AlexNet, VGG19, and LeNet which is deliberate to classify images [15].