Transfer Learning Approach for Differentiating Parkinson’s Syndromes Using Voice Recordings

Parkinson syndromes are a group of disorders affecting the elderly population with unsteadiness, slowness of activities, frequent falls, and speech disturbances, which slowly progress. Diagnosis of this group of syndromes is usually purely clinical and could be delayed due to its varied presentations. Parkinson’s syndromes comprise of Idiopathic Parkinson’s disease, Multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and cartico basal ganglionic degeneration. In this work, we provide a comparative analysis of several deep learning models such as ViT, MobileNetV2, DenseNets, ResNets, GoogLeNet, VGGs for the differentiation of Parkinson’s syndromes using prolonged vowel phonations. To address this multi-class classification problem, we employed transfer learning on DL models, by training on a dataset comprising 337 sustained vowels from patients with parkinson’s disease, MSA, PSP, and no parkinson syndromes. Each recording is transformed into a mel-spectrogram for input into the models. Among the models, ResNet152 outperformed the other models, achieving an impressive accuracy of 98.30% in classifying parkinson disorders, offering a promising non-invasive, and cost-effective diagnostic tool for early intervention and treatment planning.