Leveraging Deep Learning for Early Detection and Classification of Parkinson's Disease

Abstract

Parkinson's disease (PD) is a neurological disorder that gets worse over time and affects millions of people around the world. It is marked by motor signs like shakes, stiffness, and slow movement. Finding Parkinson's disease early is very important for improving a patient's result, but current testing methods are often subjective and only find the disease late in its progression. In this study, we suggest a new deep learning method that uses improvements in neural networks to better find and classify PD early on. This method uses complicated, high-dimensional medical data for analysis. A convolutional neural network (CNN) design is used in our method to handle speech recordings, data from walking analysis, and scribbling samples, all of which are important signs of PD. Our model is meant to find small trends and strange things that might not be obvious with regular clinical tests by using these different types of input. The suggested system is tested and trained on a large dataset that includes a wide range of patient groups. This makes sure that it is reliable and can be used in other situations. We use advanced methods like transfer learning and data augmentation to get around the problems that come up when there isn't enough labelled data. The model's performance is judged by standard measures like precision, sensitivity, and accuracy, and it is compared to other testing tools that are already out there. Early results show that our deep learning model is better at telling the difference between people with early-stage PD and healthy controls. This means that it has a lot of potential for use in clinical settings. This method not only promises to increase the number of early detections, but it also provides a scalable answer for checking many people. In the future, the model will be improved by looking into more biomarkers and doing continuous tests to see how well it can predict the future over time. This study is a big step toward making Parkinson's Disease screening tools that work better and are easier for more people to use.