Performance Enhancement of the Parkinson's Disease Classification Using Deep Learning Model

Abstract

Parkinson's Disease is a neurological condition that affects the brain. It causes tremors in the body, hands, and spine, and causes the body to stiffen. A cure or treatment is not yet available, even though the condition is quite advanced. It is only possible to treat a disease when it is in its early stages or onset. As well as cutting the disease's costs, these measures could also save a person's life. In this paper, the study will be on Parkinson's Disease and the capsule network will be used for classification of the Parkinson's Disease. Dataset used for the analysis is downloaded from the Physionet, which consists of images. Research presented here focuses on applying deep learning models to fully understand Parkinson's Disease and identify its earliest signs. In order to assess the model, we look at its precision, recall, and other related metrics. The result section has shown that the capsule network has performed better than the other existing algorithms.