Detecting COVID-19 Patients in X-Ray Images Based on MAI-Nets
COVID-19 is an infectious disease caused by virus SARS-CoV-2 virus. Early classification of COVID-19 is essential for disease cure and control. Transcription-polymerase chain reaction (RT-PCR) is used widely for the detection of COVID-19. However, its high cost, time-consuming and low sensitivity wi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2021-05-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/125957113/view |
| _version_ | 1817982991466496000 |
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| author | Wei Wang Xiao Huang Ji Li Peng Zhang Xin Wang |
| author_facet | Wei Wang Xiao Huang Ji Li Peng Zhang Xin Wang |
| author_sort | Wei Wang |
| collection | DOAJ |
| description | COVID-19 is an infectious disease caused by virus SARS-CoV-2 virus. Early classification of COVID-19 is essential for disease cure and control. Transcription-polymerase chain reaction (RT-PCR) is used widely for the detection of COVID-19. However, its high cost, time-consuming and low sensitivity will significantly reduce the diagnosis efficiency and increase the difficulty of diagnosis for COVID-19. For X-ray images of COVID-19 patients have high inter-class similarity and low intra-class variability, we specifically designed a multi attention interaction enhancement module (MAIE) and proposed a new convolutional neural network, MAI-Net, based on this module. As a lightweight network, MAI-Net has fewer layers and amount of network parameters than other network models, enabling more efficient detection of COVID-19. To verify the performance of the model, MAI-Net performed a comparison experiment on two open-source datasets. The experimental results show that its overall accuracy and COVID-19 category accuracy are 96.42% and 100%, respectively, and the sensitivity of COVID-19 is 99.02%. Considering the factors such as accuracy rate, the parameters number of network model and the calculation amount, MAI-Net has better practicability. Compared with the existing work, the network structure of MAI-Net is simpler, and the hardware requirements of the equipment are lower, which can be better used in ordinary equipment. |
| first_indexed | 2024-04-13T23:28:05Z |
| format | Article |
| id | doaj.art-9b629e18a9694f88aa92690150aaf8ce |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-13T23:28:05Z |
| publishDate | 2021-05-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-9b629e18a9694f88aa92690150aaf8ce2022-12-22T02:25:00ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832021-05-0114110.2991/ijcis.d.210518.001Detecting COVID-19 Patients in X-Ray Images Based on MAI-NetsWei WangXiao HuangJi LiPeng ZhangXin WangCOVID-19 is an infectious disease caused by virus SARS-CoV-2 virus. Early classification of COVID-19 is essential for disease cure and control. Transcription-polymerase chain reaction (RT-PCR) is used widely for the detection of COVID-19. However, its high cost, time-consuming and low sensitivity will significantly reduce the diagnosis efficiency and increase the difficulty of diagnosis for COVID-19. For X-ray images of COVID-19 patients have high inter-class similarity and low intra-class variability, we specifically designed a multi attention interaction enhancement module (MAIE) and proposed a new convolutional neural network, MAI-Net, based on this module. As a lightweight network, MAI-Net has fewer layers and amount of network parameters than other network models, enabling more efficient detection of COVID-19. To verify the performance of the model, MAI-Net performed a comparison experiment on two open-source datasets. The experimental results show that its overall accuracy and COVID-19 category accuracy are 96.42% and 100%, respectively, and the sensitivity of COVID-19 is 99.02%. Considering the factors such as accuracy rate, the parameters number of network model and the calculation amount, MAI-Net has better practicability. Compared with the existing work, the network structure of MAI-Net is simpler, and the hardware requirements of the equipment are lower, which can be better used in ordinary equipment.https://www.atlantis-press.com/article/125957113/viewCOVID-19Deep learningMAI-NetConvolutional neural networkChest X-ray images |
| spellingShingle | Wei Wang Xiao Huang Ji Li Peng Zhang Xin Wang Detecting COVID-19 Patients in X-Ray Images Based on MAI-Nets International Journal of Computational Intelligence Systems COVID-19 Deep learning MAI-Net Convolutional neural network Chest X-ray images |
| title | Detecting COVID-19 Patients in X-Ray Images Based on MAI-Nets |
| title_full | Detecting COVID-19 Patients in X-Ray Images Based on MAI-Nets |
| title_fullStr | Detecting COVID-19 Patients in X-Ray Images Based on MAI-Nets |
| title_full_unstemmed | Detecting COVID-19 Patients in X-Ray Images Based on MAI-Nets |
| title_short | Detecting COVID-19 Patients in X-Ray Images Based on MAI-Nets |
| title_sort | detecting covid 19 patients in x ray images based on mai nets |
| topic | COVID-19 Deep learning MAI-Net Convolutional neural network Chest X-ray images |
| url | https://www.atlantis-press.com/article/125957113/view |
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