ResNet-Based Parkinson's Disease Classification

Abstract

Parkinson&#x0027;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 paper, we propose a method that uses ResNet50, a Residual Network that has 50 layers, to help diagnosis PD. The data used is 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-dimensional 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-art methods. The results have demonstrated the feasibility and robustness of the proposed method. <i>Impact Statement</i> &#x2014; Parkinson&#x0027;s Disease (PD) is the most common movement disorder with more than 10 million PD patients worldwide. In the United States, around 60,000 are diagnosed with PD every year. Treatments are more effective at early stage, so early diagnosis is very important. A simple and effective classifier to diagnose PD patients is therefore crucial for doctors and patients. For the last decade, Convolutional Neural Networks (CNN) are the dominant approach for image classifications. Our proposed method benefits from the versatility, solid performance of pretrained CNN architecture by converting the frequency domain features of patients' speech recordings into heat maps. Our method achieved an accuracy of 90.7&#x0025; in diagnosis of PD by only using Tunable Q-Factor Wavelet Transform (TQWT) features, outperforming other state-of-art methods. Therefore, the conversion from non-graphical features to heat maps provides simple, accurate deep network for PD diagnosis that is fast to train, and only requires TQWT features.