COVID-19 Diagnosis System using SimpNet Deep Model
After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including...
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| Format: | Article |
| Language: | English |
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University of Baghdad, College of Science for Women
2022-10-01
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| Series: | مجلة بغداد للعلوم |
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| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6074 |
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| author | Tarza Hasan Abdullah Fattah Alizadeh Berivan Hasan Abdullah |
| author_facet | Tarza Hasan Abdullah Fattah Alizadeh Berivan Hasan Abdullah |
| author_sort | Tarza Hasan Abdullah |
| collection | DOAJ |
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After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings, and Pneumonia) classification tasks. Our model has achieved an accuracy value of 98.4% for binary and 93.8% for the multi-class classification. The number of parameters of our model is 11 Million parameters which are fewer than some state-of-the-art methods with achieving higher results.
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| format | Article |
| id | doaj-art-3bd86e6d4d3a4fbaba3a491d3e81e8c6 |
| institution | Kabale University |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2022-10-01 |
| publisher | University of Baghdad, College of Science for Women |
| record_format | Article |
| series | مجلة بغداد للعلوم |
| spelling | doaj-art-3bd86e6d4d3a4fbaba3a491d3e81e8c62025-08-20T03:33:14ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862022-10-0119510.21123/bsj.2022.6074COVID-19 Diagnosis System using SimpNet Deep ModelTarza Hasan Abdullah0Fattah Alizadeh1Berivan Hasan Abdullah2Department of Computer Science and Information Technology, College of Science, University of Salahaddin Erbil, Erbil, IraqDepartment of Computer Engineering, School of Science and Engineering, University of Kurdistan Hewler, Erbil, IraqDepartment of Medicine, Hawler Medical University, Erbil, Iraq After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings, and Pneumonia) classification tasks. Our model has achieved an accuracy value of 98.4% for binary and 93.8% for the multi-class classification. The number of parameters of our model is 11 Million parameters which are fewer than some state-of-the-art methods with achieving higher results. https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6074COVID-19, Deep Learning, SimpNet, X-ray Images |
| spellingShingle | Tarza Hasan Abdullah Fattah Alizadeh Berivan Hasan Abdullah COVID-19 Diagnosis System using SimpNet Deep Model مجلة بغداد للعلوم COVID-19, Deep Learning, SimpNet, X-ray Images |
| title | COVID-19 Diagnosis System using SimpNet Deep Model |
| title_full | COVID-19 Diagnosis System using SimpNet Deep Model |
| title_fullStr | COVID-19 Diagnosis System using SimpNet Deep Model |
| title_full_unstemmed | COVID-19 Diagnosis System using SimpNet Deep Model |
| title_short | COVID-19 Diagnosis System using SimpNet Deep Model |
| title_sort | covid 19 diagnosis system using simpnet deep model |
| topic | COVID-19, Deep Learning, SimpNet, X-ray Images |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6074 |
| work_keys_str_mv | AT tarzahasanabdullah covid19diagnosissystemusingsimpnetdeepmodel AT fattahalizadeh covid19diagnosissystemusingsimpnetdeepmodel AT berivanhasanabdullah covid19diagnosissystemusingsimpnetdeepmodel |