AG-PDCnet: An Attention Guided Parkinson’s Disease Classification Network with MRI, DTI and Clinical Assessment Data

Parkinson’s disease (PD) is the second most neurodegenerative disorder, which is prevalent worldwide. In this paper, we propose AG-PDCNet, an Attention Guided multi-class multi-modal PD Classification framework. In particular, we combine clinical assessments with the Neuroimaging data, namely, MRI and DTI. The three classes considered for this problem are PD, Healthy Controls (HC) and Scans Without Evidence of Dopamine Deficiency (SWEDD). Four CNNs, each boosted with an attention mechanism, are trained on gray matter (GM) and white matter (WM) from the MRI, and mean diffusivity (MD) and fractional anisotropy (FA) from the DTI. XGboost is employed for classification from the clinical data. At the decision level, the outputs of all the five models, four CNNs and the XGboost, are fused with an optimal weighted average fusion (OWAF) technique. Publicly available PPMI database is used for evaluation, yielding an accuracy of 96.93% for the three-class classification. Extensive comparisons, including ablation studies, are conducted to validate the effectiveness of our proposed solution.