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
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...
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MDPI AG
2020-10-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/25/20/4792 |
| _version_ | 1827704010922000384 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Shih-Yen Hsu |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-03-10T15:30:47Z |
| format | Article |
| id | doaj.art-ab2bbe1d637f4c6a95f4952f8c50ad66 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-10T15:30:47Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-ab2bbe1d637f4c6a95f4952f8c50ad662023-11-20T17:39:43ZengMDPI AGMolecules1420-30492020-10-012520479210.3390/molecules25204792Classification of the Multiple Stages of Parkinson’s Disease by a Deep Convolution Neural Network Based on <sup>99m</sup>Tc-TRODAT-1 SPECT ImagesShih-Yen Hsu0Li-Ren Yeh1Tai-Been Chen2Wei-Chang Du3Yung-Hui Huang4Wen-Hung Twan5Ming-Chia Lin6Yun-Hsuan Hsu7Yi-Chen Wu8Huei-Yung Chen9Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road., Jiao-su Village Yan-chao District, Kaohsiung City 82445, TaiwanDepartment of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road., Jiao-su Village Yan-chao District, Kaohsiung City 82445, TaiwanDepartment of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road., Jiao-su Village Yan-chao District, Kaohsiung City 82445, TaiwanDepartment of Information Engineering, I-Shou University, No.1, Sec. 1, Syuecheng Road., Dashu District, Kaohsiung 84001, TaiwanDepartment of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road., Jiao-su Village Yan-chao District, Kaohsiung City 82445, TaiwanDepartment of Life Sciences, National Taitung University, No.369, Sec. 2, University Road, Taitung 95092, TaiwanDepartment of Nuclear Medicine, E-DA Hospital, I-Shou University, No.1, Yida Rd, Jiao-su Village, Yan-chao District, Kaohsiung 82445, TaiwanDepartment of Nuclear Medicine, E-DA Hospital, I-Shou University, No.1, Yida Rd, Jiao-su Village, Yan-chao District, Kaohsiung 82445, TaiwanDepartment of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road., Jiao-su Village Yan-chao District, Kaohsiung City 82445, TaiwanDepartment of Nuclear Medicine, E-DA Hospital, I-Shou University, No.1, Yida Rd, Jiao-su Village, Yan-chao District, Kaohsiung 82445, TaiwanSingle 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).https://www.mdpi.com/1420-3049/25/20/4792SPECTParkinson’s diseasedeep learningconvolution neural network |
| spellingShingle | 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 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 Molecules SPECT Parkinson’s disease deep learning convolution neural network |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | SPECT Parkinson’s disease deep learning convolution neural network |
| url | https://www.mdpi.com/1420-3049/25/20/4792 |
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