Detection of Parkinson's Disease and Its Severity Using Deep Learning

Parkinson's disease (PD) is a disorder of the nervous system that affects about 6 million people all over the world. At the moment, there is no approved test for people in the early stages of PD. The number of lives has gone up because of this. So, it is very important to come up with a way to find Parkinson's disease early. It has been shown that the intensity of Parkinson's disease (PD) is closely related to how badly a person's handwriting is affected by the disease. Patients with Parkinson's disease can write much more slowly and with much less pressure than healthy people. So, figuring out what's wrong as soon as possible will lead to a more accurate clinical diagnosis. Because of this, a system plan is suggested to study wave and spiral drawing patterns in both people with PD and healthy people. This method uses two different convolution neural networks, one for looking at how spiral drawings are made and the other for looking at how wave drawings are made. The neural network that was suggested for intensity classification was built with Python's TensorFlow deep learning framework.