Brain Tumor Classification using Deep Learning

Abstract

The development of abnormal brain cells, some of which may turn cancerous, is known as a brain tumor. A magnetic resonance imaging (MRI) scan is the most common technique for finding brain tumors. Information about abnormal tissue growth in the brain can be discerned from MRI images. Misdiagnosis of a brain tumor will lead to ineffective medical intervention and lower patient survival rates. A proper treatment plan must begin with the accurate identification of a brain tumor in order to cure and prolong the lives of patients with this disease. Convolutional Neural Networks (CNNs) and computer-aided tumor detection systems have significantly advanced machine learning and offered breakthroughs. Many research papers use machine learning and deep learning algorithms to detect brain tumors. Brain tumor prediction takes very little time when these algorithms are applied to MRI images, and the higher accuracy makes it easier to treat patients. The radiologist can make quick decisions thanks to these predictions. seamlessly. This system ensures correct and authentic stem products. This proposed work presents a complete brain tumor detection, classification and diagnosis system with high accuracy (99.3%) that uses deep learning methods.