Segmentation and classification of specific pattern of Brain tumor using CNN

A Brain tumor is a growth of cells in the brain or near it. Brain tumors can happen in the brain tissue close by locations include nerves, the pituitary gland, central nervous system , meninges and the membranes that cover the surface of the brain[1]. A glioma is a tumor that forms in the brain or spinal cord A glioma is a tumor of the central nervous system that makes an appearance from glial stem or progenitor cells. Glial cells are a type of cell generally present in the nervous system. Gliomas predominantly occur in the brain and, rarely, in the spinal cord. They grow in approximately 6.6 per 100,000 individuals each year. They transpire at various ages, controlled by the subtype. Growing gliomas can compact areas of the brain where they occur and cause various symptoms including headaches, nausea, vomiting, cognitive impairment, seizures, gait imbalance, language impairment [2] A meningioma is a tumor that makes an appearance from the meninges — the membranes that surround the brain and spinal cord. In spite of the fact that not technically a brain tumor, it is included in this category because it may compress or squeeze the adjacent brain, nerves and vessels Meningiomas are tumors of the meninges, mostly benign and often managed by surgical resection, with radiation therapy and chemotherapy reserved for high-risk or refractory disease.[3]. Tumors can start nearly anywhere in the body. Tumors that start in the pituitary gland are called pituitary tumors. The anterior pituitary tumors start in the larger, front part of the pituitary gland known as the anterior pituitary. The posterior pituitary: The smaller, back part of the pituitary gland is an extension of brain tissue from the hypothalamus. The posterior pituitary stores and releases hormones made by the hypothalamus (vasopressin and oxytocin) into the bloodstream [4]. Machine learning applications in healthcare are already having a positive impact, and its potential is still in the early stages of being realized to deliver care[2]. In the future, machine learning in healthcare will become increasingly important as we strive to make sense of the ever-growing clinical data sets in healthcare. Heart disease cancer, and brain tumors are diagnosed using medical imaging procedures such as MRI scans, CT scans, and ECG. As a result, deep learning assists doctors in better analyzing diseases and providing the best treatment to patients. In this paper specific brain tumor segmentation and classification have been done using a Deep Convolutional Neural Network that incorporates a multiscale approach. . The proposed neural model can look over MRI images containing three types of tumors: glioma, meningioma, and pituitary tumor. Data has been collected from https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset?select=Training This dataset ecompasses 7023 images of human brain MRI images which are classified into 4 classes: glioma - meningioma - no tumor and pituitary. no tumor class images were taken from the Br35H dataset. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical data to promote the production of research results that are beneficial to doctors and patients.