Residual learning diagnosis detection: an advanced residual learning diagnosis detection system for COVID-19 in industrial internet of things
Due to the fast transmission speed and severe health damage, COVID-19 has attracted global attention. Early diagnosis and isolation are effective and imperative strategies for epidemic prevention and control. Most diagnostic methods for the COVID-19 is based on nucleic acid testing (NAT), which is e...
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/160275 |
| _version_ | 1811697718266953728 |
|---|---|
| author | Zhang, Mingdong Chu, Ronghe Dong, Chaoyu Wei, Jianguo Lu, Wenhuan Xiong, Naixue |
| author2 | Energy Research Institute @ NTU (ERI@N) |
| author_facet | Energy Research Institute @ NTU (ERI@N) Zhang, Mingdong Chu, Ronghe Dong, Chaoyu Wei, Jianguo Lu, Wenhuan Xiong, Naixue |
| author_sort | Zhang, Mingdong |
| collection | NTU |
| description | Due to the fast transmission speed and severe health damage, COVID-19 has attracted global attention. Early diagnosis and isolation are effective and imperative strategies for epidemic prevention and control. Most diagnostic methods for the COVID-19 is based on nucleic acid testing (NAT), which is expensive and time-consuming. To build an efficient and valid alternative of NAT, this article investigates the feasibility of employing computed tomography images of lungs as the diagnostic signals. Unlike normal lungs, parts of the lungs infected with the COVID-19 developed lesions, ground-glass opacity, and bronchiectasis became apparent. Through a public dataset, in this article, we propose an advanced residual learning diagnosis detection (RLDD) scheme for the COVID-19 technique, which is designed to distinguish positive COVID-19 cases from heterogeneous lung images. Besides the advantage of high diagnosis effectiveness, the designed residual-based COVID-19 detection network can efficiently extract the lung features through small COVID-19 samples, which removes the pretraining requirement on other medical datasets. In the test set, we achieve an accuracy of 91.33%, a precision of 91.30%, and a recall of 90%. For the batch of 150 samples, the assessment time is only 4.7 s. Therefore, RLDD can be integrated into the application programming interface and embedded into the medical instrument to improve the detection efficiency of COVID-19. |
| first_indexed | 2024-10-01T07:59:42Z |
| format | Journal Article |
| id | ntu-10356/160275 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:59:42Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1602752022-07-18T08:22:05Z Residual learning diagnosis detection: an advanced residual learning diagnosis detection system for COVID-19 in industrial internet of things Zhang, Mingdong Chu, Ronghe Dong, Chaoyu Wei, Jianguo Lu, Wenhuan Xiong, Naixue Energy Research Institute @ NTU (ERI@N) Engineering::Computer science and engineering Convolutional Neural Network Computed Tomography Due to the fast transmission speed and severe health damage, COVID-19 has attracted global attention. Early diagnosis and isolation are effective and imperative strategies for epidemic prevention and control. Most diagnostic methods for the COVID-19 is based on nucleic acid testing (NAT), which is expensive and time-consuming. To build an efficient and valid alternative of NAT, this article investigates the feasibility of employing computed tomography images of lungs as the diagnostic signals. Unlike normal lungs, parts of the lungs infected with the COVID-19 developed lesions, ground-glass opacity, and bronchiectasis became apparent. Through a public dataset, in this article, we propose an advanced residual learning diagnosis detection (RLDD) scheme for the COVID-19 technique, which is designed to distinguish positive COVID-19 cases from heterogeneous lung images. Besides the advantage of high diagnosis effectiveness, the designed residual-based COVID-19 detection network can efficiently extract the lung features through small COVID-19 samples, which removes the pretraining requirement on other medical datasets. In the test set, we achieve an accuracy of 91.33%, a precision of 91.30%, and a recall of 90%. For the batch of 150 samples, the assessment time is only 4.7 s. Therefore, RLDD can be integrated into the application programming interface and embedded into the medical instrument to improve the detection efficiency of COVID-19. This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFC0806802, in part by the National Natural Science Foundation of China under Grant 61876131 and Grant U1936102, and in part by the Tianjin Key Project of AI under Grant 19ZXZNGX00030. 2022-07-18T08:22:05Z 2022-07-18T08:22:05Z 2021 Journal Article Zhang, M., Chu, R., Dong, C., Wei, J., Lu, W. & Xiong, N. (2021). Residual learning diagnosis detection: an advanced residual learning diagnosis detection system for COVID-19 in industrial internet of things. IEEE Transactions On Industrial Informatics, 17(9), 6510-6518. https://dx.doi.org/10.1109/TII.2021.3051952 1551-3203 https://hdl.handle.net/10356/160275 10.1109/TII.2021.3051952 2-s2.0-85099733146 9 17 6510 6518 en IEEE Transactions on Industrial Informatics © IEEE 2021. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Convolutional Neural Network Computed Tomography Zhang, Mingdong Chu, Ronghe Dong, Chaoyu Wei, Jianguo Lu, Wenhuan Xiong, Naixue Residual learning diagnosis detection: an advanced residual learning diagnosis detection system for COVID-19 in industrial internet of things |
| title | Residual learning diagnosis detection: an advanced residual learning diagnosis detection system for COVID-19 in industrial internet of things |
| title_full | Residual learning diagnosis detection: an advanced residual learning diagnosis detection system for COVID-19 in industrial internet of things |
| title_fullStr | Residual learning diagnosis detection: an advanced residual learning diagnosis detection system for COVID-19 in industrial internet of things |
| title_full_unstemmed | Residual learning diagnosis detection: an advanced residual learning diagnosis detection system for COVID-19 in industrial internet of things |
| title_short | Residual learning diagnosis detection: an advanced residual learning diagnosis detection system for COVID-19 in industrial internet of things |
| title_sort | residual learning diagnosis detection an advanced residual learning diagnosis detection system for covid 19 in industrial internet of things |
| topic | Engineering::Computer science and engineering Convolutional Neural Network Computed Tomography |
| url | https://hdl.handle.net/10356/160275 |
| work_keys_str_mv | AT zhangmingdong residuallearningdiagnosisdetectionanadvancedresiduallearningdiagnosisdetectionsystemforcovid19inindustrialinternetofthings AT churonghe residuallearningdiagnosisdetectionanadvancedresiduallearningdiagnosisdetectionsystemforcovid19inindustrialinternetofthings AT dongchaoyu residuallearningdiagnosisdetectionanadvancedresiduallearningdiagnosisdetectionsystemforcovid19inindustrialinternetofthings AT weijianguo residuallearningdiagnosisdetectionanadvancedresiduallearningdiagnosisdetectionsystemforcovid19inindustrialinternetofthings AT luwenhuan residuallearningdiagnosisdetectionanadvancedresiduallearningdiagnosisdetectionsystemforcovid19inindustrialinternetofthings AT xiongnaixue residuallearningdiagnosisdetectionanadvancedresiduallearningdiagnosisdetectionsystemforcovid19inindustrialinternetofthings |