# A Unified Deep Learning Approach for Prediction of Parkinson's Disease

arXiv: https://arxiv.org/abs/1911.10653 · HTML: https://mlsanity.com/paper/1911.10653/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis](https://mlsanity.com/paper/2106.07524/) (2021)
- [AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging](https://mlsanity.com/paper/2206.04732/) (2022)
- [AI-enabled Efficient and Safe Food Supply Chain](https://mlsanity.com/paper/2105.00333/) (2021)
- [Advancing Parkinson's Disease Progression Prediction: Comparing Long Short-Term Memory Networks and Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2412.20744/) (2024)

## Abstract (arXiv)

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.