A novel framework for the diagnosis of Parkinson’s disease using transfer learning with RESNET50 and SVM classifier

Abstract

Most Parkinson’s patients exhibit voice disorders in the early phases of the condition. Recent study on Parkinson’s disease (PD) has concentrated on identifying speech problems from pronunciation of vowels with people affected by this disease. Proposed algorithm offers analysis of time-frequency images using transfer learning methods with support vector machine (SVM) for classification of PD affected with healthy controls using residual network 50. PD morphology is preserved in 2-dimensional time-frequency graphs
that were helpful in implementing the proposed approach. The technique employs a hybrid HT/Wigner-Ville distribution to convert one-dimensional PD soundtracks to two-dimensional time-frequency (TF) graphs. After implementation of the proposed approach, classification of healthy and unhealthy controls is done using a pre-trained ResNet50 and the result is further improved through transfer learning. The features are extracted by passing preprocessed 2-D time-frequency diagrams through ResNet50’s FC1000 layer and trained using a binary nonlinear SVM classifier. The training process with 5-fold cross-validation (CV) got accuracy of 95.07% and in testing; it reached 92.13%, attaining better results.