Classification of the Multiple Stages of Parkinson’s Disease by a Deep Convolution Neural Network Based on <sup>99m</sup>Tc-TRODAT-1 SPECT Images
Shih-Yen Hsu,
Li-Ren Yeh,
Tai-Been Chen,
Wei-Chang Du,
Yung-Hui Huang,
Wen-Hung Twan,
Ming-Chia Lin,
Yun-Hsuan Hsu,
Yi-Chen Wu,
Huei-Yung Chen
Affiliations
Shih-Yen Hsu
Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road., Jiao-su Village Yan-chao District, Kaohsiung City 82445, Taiwan
Li-Ren Yeh
Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road., Jiao-su Village Yan-chao District, Kaohsiung City 82445, Taiwan
Tai-Been Chen
Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road., Jiao-su Village Yan-chao District, Kaohsiung City 82445, Taiwan
Wei-Chang Du
Department of Information Engineering, I-Shou University, No.1, Sec. 1, Syuecheng Road., Dashu District, Kaohsiung 84001, Taiwan
Yung-Hui Huang
Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road., Jiao-su Village Yan-chao District, Kaohsiung City 82445, Taiwan
Wen-Hung Twan
Department of Life Sciences, National Taitung University, No.369, Sec. 2, University Road, Taitung 95092, Taiwan
Ming-Chia Lin
Department of Nuclear Medicine, E-DA Hospital, I-Shou University, No.1, Yida Rd, Jiao-su Village, Yan-chao District, Kaohsiung 82445, Taiwan
Yun-Hsuan Hsu
Department of Nuclear Medicine, E-DA Hospital, I-Shou University, No.1, Yida Rd, Jiao-su Village, Yan-chao District, Kaohsiung 82445, Taiwan
Yi-Chen Wu
Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road., Jiao-su Village Yan-chao District, Kaohsiung City 82445, Taiwan
Huei-Yung Chen
Department of Nuclear Medicine, E-DA Hospital, I-Shou University, No.1, Yida Rd, Jiao-su Village, Yan-chao District, Kaohsiung 82445, Taiwan
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).
