ResNet-Based Parkinson's Disease Classification

Parkinson's disease (PD) is a brain disorder that leads to shaking, stiffness, and difficulty with walking, balance, and coordination. The symptoms usually begin gradually and get worse over time. Early diagnosis is very important because treatments are more effective and easier to perform during the early stages of PD. However, early diagnosis is challenging because the symptoms start gradually, and at the early stages, they are not very noticeable. In this article, we propose a method that uses ResNet50, a residual network that has 50 layers, to help diagnosis PD. The data used are a collection of frequency features acquired by applying spectral analysis strategies to the speech recordings of the patient. We then convert the frequency features into a 2-D heat map. This heat map is passed to ResNet50, which predicts whether the patient has PD or not. We have conducted experiments and compared the accuracy with several state-of-the-art methods. The results have demonstrated the feasibility and robustness of the proposed method.