Early Detection of Parkinson’s Disease from Micrographic Static Hand Drawings

Parkinson’s disease (PD) is a neurological illness that occurs by the degeneration of cells in the nervous system. Early symptoms include tremors or involuntary movements of the hands, arms, legs, and jaw. Currently, the only method to diagnose PD involves the observation of its prodromal symptoms. Moreover, detecting handwriting will work as a variable for clinitians to understand PD in patients better. With the advancement of technology, it is possible to build applications that will aid in diagnosing PD without any clinical intervention. The majority suffering from PD have handwriting abnormalities (referred to as micrographia), which is the most reported among earlier signs of the disease. So this research is undertaken by focusing on the implication of micrographia. For this purpose, handwritten images are collected from a group of 136 PD patients and 36 healthy patients. These images form a dataset of 800 images that are used to train a model which will accurately classify PD patients. To achieve this transfer learning is chosen because of its ability to produce accurate results regardless of the limited size of the dataset. Here, different models of transfer learning are trained to figure out the well-fitting model. It was observed that VGG-16 performed adequately with a training accuracy of 90.63% while a testing accuracy of 91.36%.