Diagnosis of Parkinson’s Disease at an Early Stage Using Volume Rendering SPECT Image Slices

Abstract

The non-degenerative variant called scans without evidence of dopaminergic deficit (SWEDD) is clinically analyzed and wrongly understood as Parkinson’s disease (PD) that results ineffective diagnosis of PD in the early stage. The present work is designed to improve the diagnostic accuracy at the early stage of PD from SWEDD and healthy control (HC). The volume rendering image slices are used as a novel method to achieve better diagnostic accuracy. These image slices are chosen from the single-photon emission computed tomography (SPECT) images based on their striatal uptake region, which contributes appreciated information on the shape of the striatum. Features related to surface and the shape of the striatum are calculated from the segmented region of the chosen image slices to illustrate the good amount of variations among early PD, SWEDD, and HC. Among these feature sets, the most optimized feature is selected using genetic algorithm. The performance of the classifiers like linear, radial basis function-support vector machine (RBF-SVM), extreme learning machine (ELM) activation functions, and RBF-ELM are investigated and compared based on the most optimized feature. It is noted that the RBF-ELM offers better performance with an accuracy of 98.23% than the other classifiers. This also proves that the present work is better than the previous studies. Hence, the proposed approach could act as an aid in the detection of early stage of PD.