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Neuroimaging-based diagnosis of Parkinson's disease with deep neural mapping large margin distribution machine
- Source :
- Neurocomputing. 320:141-149
- Publication Year :
- 2018
- Publisher :
- Elsevier BV, 2018.
-
Abstract
- 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.
- Subjects :
- Parkinson's disease
Margin distribution
Artificial neural network
Computer science
business.industry
Cognitive Neuroscience
Pattern recognition
02 engineering and technology
medicine.disease
Computer Science Applications
03 medical and health sciences
Kernel (linear algebra)
0302 clinical medicine
Neuroimaging
Artificial Intelligence
Kernel (statistics)
0202 electrical engineering, electronic engineering, information engineering
medicine
020201 artificial intelligence & image processing
Artificial intelligence
business
Classifier (UML)
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 320
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........3bb7a23d09eb0b092c26f701827b41a6