Classification of the Multiple Stages of Parkinson’s Disease by a Deep Convolution Neural Network Based on 99mTc-TRODAT-1 SPECT Images

Molecules

Wei-Chang Du¹  Wen-Hung Twan²  Shih-Yen Hsu³  Yi-Chen Wu³  Li-Ren Yeh³  Tai-Been Chen³  Yung-Hui Huang³  Huei-Yung Chen⁴  Yun-Hsuan Hsu⁴  Ming-Chia Lin⁴ 

[1] Department of Information Engineering, I-Shou University, No.1, Sec. 1, Syuecheng Road., Dashu District, Kaohsiung 84001, Taiwan;Department of Life Sciences, National Taitung University, No.369, Sec. 2, University Road, Taitung 95092, Taiwan;Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road., Jiao-su Village Yan-chao District, Kaohsiung City 82445, Taiwan;Department of Nuclear Medicine, E-DA Hospital, I-Shou University, No.1, Yida Rd, Jiao-su Village, Yan-chao District, Kaohsiung 82445, Taiwan;

关键词: SPECT;    Parkinson’s disease;    deep learning;    convolution neural network;   

DOI  :  10.3390/molecules25204792

来源: DOAJ

【 摘 要 】

Single photon emission computed tomography (SPECT) has been employed to detect Parkinson’s disease (PD). However, analysis of the SPECT PD images was mostly based on the region of interest (ROI) approach. Due to limited size of the ROI, especially in the multi-stage classification of PD, this study utilizes deep learning methods to establish a multiple stages classification model of PD. In the retrospective study, the 99mTc-TRODAT-1 was used for brain SPECT imaging. A total of 202 cases were collected, and five slices were selected for analysis from each subject. The total number of images was thus 1010. According to the Hoehn and Yahr Scale standards, all the cases were divided into healthy, early, middle, late four stages, and HYS I~V six stages. Deep learning is compared with five convolutional neural networks (CNNs). The input images included grayscale and pseudo color of two types. The training and validation sets were 70% and 30%. The accuracy, recall, precision, F-score, and Kappa values were used to evaluate the models’ performance. The best accuracy of the models based on grayscale and color images in four and six stages were 0.83 (AlexNet), 0.85 (VGG), 0.78 (DenseNet) and 0.78 (DenseNet).

【 授权许可】

Unknown