# An ordinal CNN approach for the assessment of neurological damage in Parkinson's disease patients

arXiv: https://arxiv.org/abs/2106.05230 · HTML: https://mlsanity.com/paper/2106.05230/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 55 references that are in the map.
- First posted on arXiv 31 May 2021.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localization](https://mlsanity.com/paper/1908.00748/) (2019)
- [Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait](https://mlsanity.com/paper/1910.11509/) (2019)

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

- [Ordinal classification for interval-valued data and interval-valued functional data](https://mlsanity.com/paper/2310.19433/) (2023)
- [AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes](https://mlsanity.com/paper/2202.08407/) (2022)
- [Improving the classification of extreme classes by means of loss regularisation and generalised beta distributions](https://mlsanity.com/paper/2407.12417/) (2024)
- [Deep Neural Networks with Ordinal Loss for Medical Applications](https://mlsanity.com/paper/2606.25769/) (2026)

## Abstract (arXiv)

3D image scans are an assessment tool for neurological damage in Parkinson's disease (PD) patients. This diagnosis process can be automatized to help medical staff through Decision Support Systems (DSSs), and Convolutional Neural Networks (CNNs) are good candidates, because they are effective when applied to spatial data. This paper proposes a 3D CNN ordinal model for assessing the level or neurological damage in PD patients. Given that CNNs need large datasets to achieve acceptable performance, a data augmentation method is adapted to work with spatial data. We consider the Ordinal Graph-based Oversampling via Shortest Paths (OGO-SP) method, which applies a gamma probability distribution for inter-class data generation. A modification of OGO-SP is proposed, the OGO-SP-β algorithm, which applies the beta distribution for generating synthetic samples in the inter-class region, a better suited distribution when compared to gamma. The evaluation of the different methods is based on a novel 3D image dataset provided by the Hospital Universitario 'Reina Sof'ia' (C'ordoba, Spain). We show how the ordinal methodology improves the performance with respect to the nominal one, and how OGO-SP-β yields better performance than OGO-SP.