Convolutional neural network for the detection of Parkinson disease based on hand-draw spiral images

Abstract

Parkinson’s disease (PD) is a chronic and increasing sickness that hits hundreds-thousands of people globally. Patients who are infected by PD have been proven to show some common symptoms such as slowness of movement, tremors, and freezing of gait. One of the most popular exams to detect the PD is to use the handwritten assessment tool, where the individuals are asked to draw spirals on a template paper. Therefore, this study proposes a convolutional neural network algorithm for detecting the PD by utilizing the hand-draw spiral images. In the present study, balanced spiral images dataset has been utilized for both categories (i.e., Parkinson and healthy). The dataset contains 102 samples as a total number of spiral images (i.e., 51 Parkinson and 51 healthy). Moreover, numerous evaluation measurements were utilized in order to assess the proposed approach such as recall, precision, accuracy, F-measure, specificity, Matthew's correlation coefficient (MCC), and G-mean. Based on the outcomes of the experiments, the proposed approach achieves 93.33% accuracy, 86.67% specificity, 88.24% precision, 100.00% recall, 93.75% F-measure, 93.93% G-mean, and 87.45% MCC. The proposed approach demonstrates promising outcomes in the detection of PD. As well as the proposed convolutional neural network (CNN) approach was outperformed all its comparatives regarding the classification accuracy rate.