A Unified Deep Learning Approach for Prediction of Parkinson's Disease
This work addresses the challenge of reliable Parkinson's disease diagnosis for medical professionals, though it appears incremental as it builds on existing deep learning techniques.
The paper tackles the problem of diagnosing Parkinson's disease by proposing a unified deep learning framework that uses transfer learning and domain adaptation on medical images like MRIs and DaTscans, achieving effective prediction across different medical environments as demonstrated in a large experimental study.
The paper presents a novel approach, based on deep learning, for diagnosis of Parkinson's disease through medical imaging. The approach includes analysis and use of the knowledge extracted by Deep Convolutional and Recurrent Neural Networks (DNNs) when trained with medical images, such as Magnetic Resonance Images and DaTscans. Internal representations of the trained DNNs constitute the extracted knowledge which is used in a transfer learning and domain adaptation manner, so as to create a unified framework for prediction of Parkinson's across different medical environments. A large experimental study is presented illustrating the ability of the proposed approach to effectively predict Parkinson's, using different medical image sets from real environments.