Parkinson\'s Disease Detection Using Deep Learning Technique

Abstract

Abstract: Parkinson’s Disease is a degenerative nervous system ailment primarily impacting middle-aged and older persons. Tremors, stiffness of the muscles, and slow, clumsy movement mark it. Parkinson's Disease is believed to be caused by genetic and environmental factors, while its precise cause is yet unclear. Levodopa can aid patients' quality of life and manage their symptoms, but there is no proven treatment for Parkinson's Disease. A unique deep-learning approach is developed to determine whether a person has PD based on premotor traits. This study has looked explicitly at several signs to identify PD at an early stage using spiral drawing. Measuring the changes in the handwritten spiral drawing allows for the early and accurate diagnosis of PD. Deep learning algorithms have been used to track the development of the illness and the effectiveness of treatment in PD patients in addition to diagnosis and prediction. By utilizing efficient treatments and medications, the findings will showcase how early illness detection can enhance a patient's life expectancy and enable them to live peacefully. One commonly utilized deep learning technique in PD research is Convolutional Neural Networks (CNNs). This work uses Modified Convolutional Neural Networks (MCNN) to predict the normal and abnormal of Parkinson's Disease. The complete model's performance after being trained on data from 36 patients was 96% overall accuracy, with average recall, precision, and f1 scores of 96.73%, 96.05%, and 96%, respectively.