An ordinal CNN approach for the assessment of neurological damage in Parkinson's disease patients
This work addresses the need for decision support systems in medical diagnosis for Parkinson's disease, but it is incremental as it builds on existing CNN and data augmentation techniques with a specific dataset.
The paper tackled the problem of automating neurological damage assessment in Parkinson's disease patients using 3D image scans by proposing a 3D CNN ordinal model and a modified data augmentation method, OGO-SP-β, which improved performance over nominal methods and the original OGO-SP.
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 Sofía' (Córdoba, 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.