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Neuroimaging has shown its effectiveness for diagnosis of Parkinson's disease (PD), and the neuroimaging-based computer-aided diagnosis (CAD) then attracts considerable attention. In a CAD system, the classifier module is one of the key components, which directly decides the classification performance. As a newly proposed classifier, the large margin distribution machine (LDM) has excellent generalization by maximizing the margin mean and minimizing the margin variance simultaneously. However, LDM still suffers from the problem of kernel selection. In this work, we propose a deep neural mapping large margin distribution machine (DNMLDM) algorithm by adopting the deep neural network (DNN) to perform a kernel mapping instead of the implicit kernel function in LDM. A two-stage joint training strategy is then developed, including the unsupervised layer-wise pre-training for DNN and then the supervised fine-tuning for all parameters in the whole networks. Two real-world PD datasets, namely the transcranial sonography (TCS) dataset and the magnetic resonance imaging (MRI) dataset, are used to evaluate the performance of DNMLDM algorithm. The experimental results show that the proposed DNMLDM outperforms all the compared algorithms on both datasets.

Neuroimaging-based diagnosis of Parkinson's disease with deep neural mapping large margin distribution machine