ABSTRACT
A neurological 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. Neurological disorders typically worsen over time and have no known cure. Several approaches have been developed in recent times to automatically diagnose different neurological disorder conditions. These approaches can essentially be split into two types of hand-crafted features and classifier approaches based on standard instruction, respectively. The second solution is focused on completely automatic approaches based on deep learning. The first type uses manually segregated characteristics and is given to classifiers as data. In training, the classifiers do not change the functions. However, in the second category of attributes, parameters may be modified to execute unique training data activities. Deep learning does not use hand-crafted features and have successfully been adapted to solve the neurological disorder diagnostic problems. As a result, deep learning is now playing an important role in the advancement of neurological disorder diagnostics.
