Applications of Neural Networks in Brain Tumors Detection and Classification: A Review

Brain cancer is considered to be among the most deadly types of cancer, causing an average of 308,100 deaths annually as reported by the World Health Organization. Survival rates for affected individuals are relatively low, making early detection a critical priority. Currently, researchers and engineers are actively working on developing automated Computer Aided Diagnostic systems to identify various abnormalities in the brain. While there are numerous sources of brain images available, Magnetic Resonance Imaging remains the most commonly used type in existing CAD systems. This paper aims to provide an overview of new approaches utilized in detecting and classifying brain tumors through computer vision and deep learning techniques. Subsequently, these approaches will be compared with a focus on their respective advantages and disadvantages.