Multi-modal multi-class Parkinson disease classification using CNN and decision level fusion
This work addresses Parkinson's disease diagnosis, a critical neurodegenerative disorder, by providing a high-accuracy classification method, though it is incremental as it builds on existing CNN and fusion techniques.
The paper tackled the problem of classifying Parkinson's disease into three classes (PD, SWEDD, and healthy controls) using multi-modal MRI and DTI data, achieving an accuracy of 95.53% on the PPMI database.
Parkinson disease is the second most common neurodegenerative disorder, as reported by the World Health Organization. In this paper, we propose a direct three-Class PD classification using two different modalities, namely, MRI and DTI. The three classes used for classification are PD, Scans Without Evidence of Dopamine Deficit and Healthy Control. We use white matter and gray matter from the MRI and fractional anisotropy and mean diffusivity from the DTI to achieve our goal. We train four separate CNNs on the above four types of data. At the decision level, the outputs of the four CNN models are fused with an optimal weighted average fusion technique. We achieve an accuracy of 95.53 percentage for the direct three class classification of PD, HC and SWEDD on the publicly available PPMI database. Extensive comparisons including a series of ablation studies clearly demonstrate the effectiveness of our proposed solution.