Deep learning for neurodegenerative disorder (2016 to 2022): A systematic review

A neurodegenerative disorder, such as Parkinson's, Alzheimer's, epilepsy, stroke, and others, is a type of disease in which central nervous system cells stop working or die. Neurodegenerative disorders typically worsen over time and have no known cure. Because of advances in deep learning, it is now possible to detect and classify neurodegenerative disorders using an automated process that is more efficient than manual detection. Many articles have recently been published on the automatic detection, and classification of various types of neurodegenerative disorders using deep learning techniques. This paper documents the systematic reviews on the detection, and classification techniques of neurodegenerative disorder from five different facets viz., datasets and data modality of neurodegenerative disorder, pre-processing methods, deep learning-based detection and classification of neurodegenerative disorder, and performance measure matrices. It also summarizes the existing study's conclusions and the significance of the study's findings. This review provides a comprehensive description of neurodegenerative disorder classification and detection techniques that may be useful to the scientific community working on automatic neurodegenerative disorder classification and detection.