Research Paper to Detect Parkinson's Disease

Predictive markers for Parkinson's Disease using Machine Learning & Deep Learning techniques on brain MRIs.

Research Paper

Abstract

Neuromelanin sensitive magnetic resonance imaging (NMS-MRI) has been crucial in identifying abnormalities in the substantia nigra pars compacta (SNc) in Parkinson's Disease. The extraction of these features is time-consuming and laborious and moreover provides lower prediction accuracies. This work establishes a technique that uses convolutional neural networks (CNNs) to create prognostic and diagnostic biomarkers of PD from NMS-MRI.

CNN Architecture

The CNN architecture inspired by the ResNet50: CNN Architecture Diagram

Results

This approach not only performs with a superior testing accuracy (80%) as compared to contrast ratio-based classification (56.5% testing accuracy) and radiomics classifier (60.3% testing accuracy), but also supports discriminating PD from atypical parkinsonian syndromes (85.7% test accuracy).

Read the entire paper at https://pubmed.ncbi.nlm.nih.gov/30870733.