A review of Brain Cancer Detection and Classification Using Artificial Intelligence and Machine Learning

Brain cancer is a devastating and life-threatening disease that affects millions of individuals worldwide. Timely and accurate detection of brain tumors is crucial for effective treatment and patient outcomes. In recent years, there has been a growing interest in the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to improve the detection and classification of brain cancer. The integration of AI and ML into medical imaging and diagnostic processes has shown remarkable potential in enhancing the accuracy and efficiency of brain tumor diagnosis. These technologies offer the capability to analyze complex patterns and structures within medical images, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans, aiding in the early identification of brain tumors and the precise categorization of tumor types. This review aims to provide a comprehensive assessment of the current state of research in the field of Brain Cancer Detection and Classification using AI and ML. It delves into the methodologies, datasets, and performance metrics utilized in various studies. Additionally, it explores the challenges and limitations of existing approac