Review of Deep Learning Techniques for Neurological Disorders Detection

Abstract

Neurological disease is one of the most common types of dementia that predominantly concerns the elderly. In clinical approaches, identifying its premature stages is complicated, and no biomarker is comprehended to be thorough in witnessing neurological disorders in their earlier stages. Deep learning approaches have attracted much attention in the scientific community using scanned images. They differ from simple machine learning (ML) algorithms in that they study the most favorable depiction of untreated images. Deep learning is helpful in the neuroimaging analysis of neurological diseases with subtle and dispersed changes because it can discover abstract and complicated patterns. The current study discusses a vital part of deep learning and looks at past work that has been used to switch between different ML algorithms that can predict neurological diseases. Convolution Neural Networks, Generative Adversarial Network, Recurrent Neural Network, Deep Belief Network, Auto Encoder, and other algorithms for Alzheimer’s illness prediction have been considered. Many publications on preprocessing methods, such as scaling, correction, stripping, and normalizing, have been evaluated.