The Research of Multi-Modality Parkinson's Disease Image Based on Cross-Layer Convolutional Neural Network
Multi-modal diagnosis technology has become an increasingly important method in the medical field and has been widely used in imaging diagnosis and clinical treatment. This paper fused the T2-MRI and PET images of Parkinson patients. And the Multi-modality fusion images were input to
a convolutional neural network for diagnosis of Parkinson's disease. This paper designed a cross-layer convolutional neural network for Parkinson's disease. The cross-layer convolutional neural network was used to learn and test the multi-modality fusion images, PET single-mode fusion images,
T2-MRI single-mode fusion images respectively. Experiment result showed that the cross-layer convolutional neural network has higher diagnostic accuracy than the Lenet-5 convolutional neural network. And the diagnostic accuracy of multi-modality fusion images is a higher than that of single-mode
fusion images. The diagnostic accuracy of single-mode fusion PET images is higher than that of single-mode fusion T2-MRI images. The result showed that multi-modality fusion images have better auxiliary diagnostic effects than the single modal fusion images. And the auxiliary diagnosis effect
of PET single-modal fusion images is better than that of T2-MRI single-modal fusion images. The cross-layer convolutional neural network is much better than the Lenet-5 convolutional neural network. It is more suitable for the diagnosis of Parkinson's disease.
Keywords: MULTI-MODALITY IMAGING DIAGNOSIS; PARKINSON'S DISEASE; SINGLE-MODE IMAGING DIAGNOSIS; THE CONVOLUTIONAL NEURAL NETWORK
Document Type: Research Article
Publication date: 01 September 2019
- Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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