Intelligent diagnosis of coronavirus with computed tomography images using a deep learning model
The coronavirus (COVID-19) disease appeared as a respiratory system disorder and has triggered pneumonia outbreaks globally. As this COVID-19 disease drastically spread around the world, computed tomography (CT) has helped to diagnose it rapidly. It is imperative to implement a faultless computer-ai...
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Format: | Article |
Language: | English |
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SPIE
2023
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Online Access: | View Fulltext in Publisher View in Scopus |
LEADER | 02923nam a2200493Ia 4500 | ||
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001 | 10.1117-1.JEI.32.2.021406 | ||
008 | 230529s2023 CNT 000 0 und d | ||
020 | |a 10179909 (ISSN) | ||
245 | 1 | 0 | |a Intelligent diagnosis of coronavirus with computed tomography images using a deep learning model |
260 | 0 | |b SPIE |c 2023 | |
856 | |z View Fulltext in Publisher |u https://doi.org/10.1117/1.JEI.32.2.021406 | ||
856 | |z View in Scopus |u https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159218794&doi=10.1117%2f1.JEI.32.2.021406&partnerID=40&md5=e00cd3c386c75bd882028a039616f6a3 | ||
520 | 3 | |a The coronavirus (COVID-19) disease appeared as a respiratory system disorder and has triggered pneumonia outbreaks globally. As this COVID-19 disease drastically spread around the world, computed tomography (CT) has helped to diagnose it rapidly. It is imperative to implement a faultless computer-aided model for detecting COVID-19-affected patients through CT images. Therefore, a detail extraction pyramid network (DEPNet) is proposed to predict COVID-19-affected cases from CT images of the COVID-CT-MD dataset. In this study, the COVID-CT-MD dataset is applied to detect the accuracy of the deep learning technique; the dataset has CT scans of 169 patients; among those, 60 patients are COVID-19 positive patients, and 76 cases are normal. These affected patients were clinically verified with the standard hospital. The deep learning-oriented CT diagnosis model is implemented to detect COVID-19-affected patients. The experiment revealed that the proposed model categorized COVID-19 cases from other respiratory-oriented diseases with 99.45% accuracy. Further, this model selected the exact lesion parts, mainly ground-glass opacity, which helped the doctors to diagnose visually. © 2022 SPIE and IS&T. | |
650 | 0 | 4 | |a Computed tomography images |
650 | 0 | 4 | |a Computed tomography lung image |
650 | 0 | 4 | |a computed tomography lung images |
650 | 0 | 4 | |a Computerized tomography |
650 | 0 | 4 | |a coronavirus |
650 | 0 | 4 | |a Coronavirus |
650 | 0 | 4 | |a Coronaviruses |
650 | 0 | 4 | |a COVID-19 |
650 | 0 | 4 | |a deep learning |
650 | 0 | 4 | |a Deep learning |
650 | 0 | 4 | |a detail extraction pyramid network |
650 | 0 | 4 | |a Detail extraction pyramid network |
650 | 0 | 4 | |a Details extractions |
650 | 0 | 4 | |a Diagnosis |
650 | 0 | 4 | |a Extraction |
650 | 0 | 4 | |a Intelligent diagnosis |
650 | 0 | 4 | |a Learning models |
650 | 0 | 4 | |a Learning systems |
650 | 0 | 4 | |a Medical computing |
650 | 0 | 4 | |a Pulmonary diseases |
650 | 0 | 4 | |a Pyramid network |
650 | 0 | 4 | |a Respiratory system |
650 | 0 | 4 | |a severe acute respiratory syndrome |
650 | 0 | 4 | |a Severe acute respiratory syndrome |
700 | 1 | 0 | |a Bacanin, N. |e author |
700 | 1 | 0 | |a Jovanovic, D. |e author |
700 | 1 | 0 | |a Mravik, M. |e author |
700 | 1 | 0 | |a Sarac, M. |e author |
700 | 1 | 0 | |a Strumberger, I. |e author |
700 | 1 | 0 | |a Zivkovic, M. |e author |
773 | |t Journal of Electronic Imaging |