Multi-modal multi-class Parkinson disease classification using CNN and
  decision level fusion
Sushanta Kumar Sahu Ananda S. Chowdhury
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.