Parkinson's Disease Detection Using Ensemble Architecture from MR Images
This work addresses Parkinson's Disease diagnosis for patients over 60, but it is incremental as it applies existing CNN models to a specific medical imaging task.
The paper tackled Parkinson's Disease detection from MR images by focusing on Gray Matter and White Matter regions instead of whole images, achieving an average accuracy of 94.7% with a proposed ensemble architecture.
Parkinson's Disease(PD) is one of the major nervous system disorders that affect people over 60. PD can cause cognitive impairments. In this work, we explore various approaches to identify Parkinson's using Magnetic Resonance (MR) T1 images of the brain. We experiment with ensemble architectures combining some winning Convolutional Neural Network models of ImageNet Large Scale Visual Recognition Challenge (ILSVRC) and propose two architectures. We find that detection accuracy increases drastically when we focus on the Gray Matter (GM) and White Matter (WM) regions from the MR images instead of using whole MR images. We achieved an average accuracy of 94.7\% using smoothed GM and WM extracts and one of our proposed architectures. We also perform occlusion analysis and determine which brain areas are relevant in the architecture decision making process.