An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagery

TL;DR

This paper presents an explainable machine learning approach using LIME and CNNs to accurately classify DaTscan images for early Parkinson's detection, providing visual explanations to aid medical diagnosis.

Contribution

It introduces a transfer learning-based CNN model with LIME explanations for interpretability in Parkinson's diagnosis from DaTscan images, enhancing early detection methods.

Findings

Accuracy of 95.2% in classification

High sensitivity of 97.5%

Effective visual interpretability with LIME

Abstract

Parkinson's disease (PD) is a degenerative and progressive neurological condition. Early diagnosis can improve treatment for patients and is performed through dopaminergic imaging techniques like the SPECT DaTscan. In this study, we propose a machine learning model that accurately classifies any given DaTscan as having Parkinson's disease or not, in addition to providing a plausible reason for the prediction. This is kind of reasoning is done through the use of visual indicators generated using Local Interpretable Model-Agnostic Explainer (LIME) methods. DaTscans were drawn from the Parkinson's Progression Markers Initiative database and trained on a CNN (VGG16) using transfer learning, yielding an accuracy of 95.2%, a sensitivity of 97.5%, and a specificity of 90.9%. Keeping model interpretability of paramount importance, especially in the healthcare field, this study utilises LIME explanations to distinguish PD from non-PD, using visual superpixels on the DaTscans. It could be concluded that the proposed system, in union with its measured interpretability and accuracy may effectively aid medical workers in the early diagnosis of Parkinson's Disease.