# Parkinson's Disease Diagnosis Using Deep Learning

arXiv: https://arxiv.org/abs/2101.05631 · HTML: https://mlsanity.com/paper/2101.05631/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 54 references that are in the map.
- First posted on arXiv 3 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [Learning to Diagnose with LSTM Recurrent Neural Networks](https://mlsanity.com/paper/1511.03677/) (2015)
- [On the Origin of Deep Learning](https://mlsanity.com/paper/1702.07800/) (2017)

## Abstract (arXiv)

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.