Abstract
Parkinson's disease (PD) is a common chronic occult neurological degeneration disease which is most likely to occur among middle-aged and old-aged people. Neuroprotective methods can significantly slow the PD progression, therefore early diagnosis and the treatment of the PD is crucial. As the second most common neurodegenerative disorder, Parkinson's disease has its identification based on clinical diagnoses. Early diagnosis is especially important due to the lack of radical treatment. In all early diagnostic methods, results of imaging diagnosis based on positron emission tomography (PET) imaging has the most outstanding accuracy. Deep learning methods based on convolutional neural networks performed well in diagnosing different diseases. To address the demand of early diagnosis, this paper applies several preprocessing methods to image data such as gray level transformation, histogram equalization, improved wavelet soft-threshold denoising and image enhancement, and proposes a deep learning model based on U-Net architecture with deformable convolution kernels. After comparing the results of both methods, we found out that deformable U-Net outperformed the previously built improved VGG-Net. These results show that U-Net with deformable convolution structure has a good diagnostic capability and a better performance than the improved VGG-Net.
Highlights
In this study, gray level transformation, histogram equalization, improved wavelet soft-threshold denoising and image enhancement were used for image preprocessing, the convolutional neural network was trained and tested according to the positron emission tomography (PET) image sets collected from Parkinson patients
According to the test results of neural network models, it can be concluded that U-Net performs better than the Convolutional Neural Network (CNN) in the task of Parkinson’s Disease (PD) diagnosis using PET images
To make trained convolutional neural network model perform more accurate disease diagnosis results, the network can automatically carry out characteristic learning, adjust weight and offset parameters during the diagnosis of Parkinson’s disease
Summary
A. STATUS OF PARKINSON’S DISEASE Parkinson’s Disease (PD) is a common chronic occult neurological degeneration disease which is most likely to occur among middle-aged and old-aged people. Clinical symptoms of PD can be divided into motor symptoms, which include resting tremor, bradykinesia, postural instability, etc., and non-motor symptoms [1], which include mental symptom, cognitive function change, language barrier and autonomic nervous dysfunction [2]. Most PD clinical symptoms are not obvious. It’s difficult to make accurate diagnosis while only depending on its clinical manifestations and series routine examinations. When dopaminergic neurons in substantia nigra of brain decreases by 30% to 70%
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