An ensemble of ResNet model for classification of Parkinson disease

Abstract

Parkinson disease (PD) is one of the neurological illnesses incurred. However, there is a no chance to recognize PD. A fine motor symptom has been identified in this study. A group of patients with PD, as well as the healthy group, is used in the research. The authors have developed a technique that can determine whether a patient has PD or not. Using deep learning methods, the same design generalizing neural networks in the brain can be solved. The categorization of patients with PD and non-PD behavior is found from the analysis of spiral and wave forms using CNN model. Various CNN models were used in the experiment by transfer learning and spiral and wave data sketches. With the help of spiral sketching, the system achieved an accuracy of 96.67% using the ResNet50 model. The main objective of this paper is to explore the application of transfer learning, which improved the performance of the model.