Deep Learning and Machine Learning Approaches for Brain Tumor Detection and Classification

According to the International Agency for Research on Cancer (IARC), brain tumours have a 76% mortality rate. The only way to preserve a patient's life is to immediately diagnose and treat their brain tumour. Owing to improvements in the design of computers, images from diagnostic devices like computed tomography (CT) and magnetic resonance imaging (MRI) can now be analyzed without the need for human interaction. In particular, Convolutional Neural Networks' (CNN) ability to effectively process and identify vast volumes of complex visual data has made them a favorite among medical experts. This work goal is to provide a comprehensive overview of the evolution of key data science disciplines like preprocessing, machine, and deep learning and to make insightful comparisons between them. Several studies have examined a vast array of methods for the accurate and rapid detection of brain tumors. Deep Learning (DL) techniques have lately gained popularity for creating automated systems that can quickly and reliably segment or diagnose brain tumours. The key objective of this research is to develop a superior model in the hopes of improving the efficiency with which brain tumors can be identified.