Parkinson's Disease Diagnosis Using Deep Learning
This project addresses the challenging task of accurately diagnosing Parkinson's Disease for medical professionals, which is crucial due to symptom overlap with other conditions.
This project aims to automate Parkinson's Disease (PD) diagnosis using deep learning, specifically Recursive Neural Networks (RNN) and Convolutional Neural Networks (CNN), to distinguish between healthy and PD patients. It also explores the effectiveness of different PD tests and dataset types (imaging vs. time series) in the discrimination process.
Parkinson's Disease (PD) is a chronic, degenerative disorder which leads to a range of motor and cognitive symptoms. PD diagnosis is a challenging task since its symptoms are very similar to other diseases such as normal ageing and essential tremor. Much research has been applied to diagnosing this disease. This project aims to automate the PD diagnosis process using deep learning, Recursive Neural Networks (RNN) and Convolutional Neural Networks (CNN), to differentiate between healthy and PD patients. Besides that, since different datasets may capture different aspects of this disease, this project aims to explore which PD test is more effective in the discrimination process by analysing different imaging and movement datasets (notably cube and spiral pentagon datasets). In addition, this project evaluates which dataset type, imaging or time series, is more effective in diagnosing PD.