Classify Parkinson Disease from MRI Sample Based on Hybrid Feature Extraction Method

A neurological illness known as Parkinson disease (PD) that affects humans through the nerve cells of the neurological system aging. Unmanageable tremors or jaw, arm, leg, or hand motions are the possible signs. Currently, the only way to diagnose PD is to keep an eye out for its prodromal signs. The speed of therapy can be accelerated by a physician’s expertise being increased by an automated classification approach. This study’s goal is to refine an automated Parkinson’s disease classifier using artificial neural networks (ANNs) and a technique for extracting hybrid features. The prepossessing of the images serves as the initial step in the classification strategy. The analytical properties of the prepossessed pictures are takeout using a mixed feature extraction method that combines stationary wavelet transform (SWT) and gray-level co-occurrence matrix (GLCM) approaches to increase the effectiveness of the classification process. In order to determine, if a patient has Parkinson disease or not, the ANN is used as the final step. The chosen strategy replaces the typical discrete wavelet transform (DWT) method with a blended approach of feature extraction based on SWT, leading to an improved accuracy of classification which is of 84.1%, which is significantly better than modern multi-classification methods.