A comparative analysis of machine learning algorithms to classify brain diseases

Brain diseases are neurodegenerative diseases that cause nerves and brains to degenerate over time. The term "neurodegenerative" refers to the loss of brain cells in areas of the brain where dopamine is normally produced. It causes confusion, alters personality, and then destroys brain nerves and tissues. Magnetic Resonance Imaging (MRI) is a popular radiology technique for diagnosing brain diseases. The goal of this review is to present the main research methods of machine learning-based classification for brain diseases, as well as to provide relevant learning and reference for interested researchers. Meanwhile, it summarises the major flaws in existing methods and provides better direction for future research. The taxonomy of existing brain disease detection methods is also presented, as are the problems that have arisen as a result of the existing methods. The identification methods of machine learning that minimize the difficulty for humans can be used to understand the automatic classification and detection of brain diseases. This review lays the groundwork for effective clinical diagnosis, treatment planning, and precise quantitative evaluation of brain disease detection.