# A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images
## 1 Introduction  
A brain tumor is a fatal disease-causing death to thousands of people around the globe. A brain tumor is mainly caused by abnormal growth in brain tissues. As the skull portion of the human body is inflexible and small, any growth inside the brain may affect the functionality of the human organ depending on its origin and position. Moreover, it may also spread in other parts of the body and affect their functionality [1]. Usually, the brain tumor is categorized into two classes, named primary and secondary based on its position. The primary tumor comprises 70% of all brain tumors while the remaining 30% are secondary [2]. A primary brain tumor includes tumors that originate from the brain cells while a secondary brain tumor first originates in another organ and then transfers to the brain through the circulation of the blood [3]. According to an NBTF study, in the USA an estimated 29,000 cases are diagnosed with primary brain tumor, among which,around 13,000 patients die per year [4]. Similarly, in the UK, more than 42,000 patients with a primary brain tumor die annually. Among various primary brain tumor types, glioma has the highest mortality and mobility rate [5]. Gliomas usually grow from glial cells of the brain [1] and are classified as low-grade (LG) glioma and high-grade (HG) glioma. The HG glioma is more life-threatening and intense and usually, the victim can survive for two years [6]. A meningioma tumor usually develops in the protective membrane layer which acts as a covering of the human brain and spinal cord. Mostly, meningioma tumors are less threatening and slow-growing [7]. The pituitary tumor starts developing in the pituitary gland, which is located at the base of the brain and is involved in the production of several essential hormones in the body [8]. A pituitary tumor is a benign tumor; however, serious complications may cause hormonal deficiencies or permanent loss of vision because of the overproduction of hormones [1]. Hence, an early-stage detection of brain tumors is critical and is of extreme clinical interest. If it is not diagnosed on time, the disease could become life-threatening or may result in the disability of a person [7]. Depending on the situation and their purpose, numerous medical imaging techniques can be used in clinical practices for tumor diagnosis [9]. Some of those techniques are ultrasonography (US), magnetic resonance imaging (MRI), and computed tomography (CT) [10]. The MRI is the most common non-invasive imaging technique because it uses no damaging ionizing radiations of X-rays during the scan. Moreover, it provides high-quality images of soft tissue without any risk, plus an ability to acquire multiple modalities, e.g., T1, T1c, T2, and FLAIR, using various parameters. Each of these modalities produces noticeably a unique tissue contrast [11]. For timely treatment, the top priority of a neurosurgeon is to mark out tumor regions as precisely as possible, otherwise excessive or insufficient cutting may lead to suffering or a permanent loss. Unfortunately, this manual segmentation process is laborious and time-consuming and yields poor segmentation results [12]. Hence, the use of computer-aided brain tumor segmentation algorithms using MRI to identify and segment brain tumors has received considerable attention from the research community. Therefore, there is a significant need for automated and efficient tumor detection and segmentation technique. Despite recent developments in automatic or semi-automatic techniques for tumor segmentation, it is still a challenging task to segment a tumor accurately because of the following reasons [13]. First, there is a significant change in tumor location, shape, appearance, and size from patient to patient [14]. Second, the tumor boundaries can be discontinuous or blurry as the tumor regions are usually occupied by surrounding healthy tissues [15].