Challenges, opportunities, and advances related to COVID-19 classification based on deep learning
The novel coronavirus disease, or COVID-19, is a hazardous disease. It is endangering the lives of many people living in more than two hundred countries. It directly affects the lungs. In general, two main imaging modalities, i.e., computed tomography (CT) and chest x-ray (CXR) are used to achieve a...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2023-06-01
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| Series: | Data Science and Management |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666764923000188 |
| _version_ | 1797787222796140544 |
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| author | Abhishek Agnihotri Narendra Kohli |
| author_facet | Abhishek Agnihotri Narendra Kohli |
| author_sort | Abhishek Agnihotri |
| collection | DOAJ |
| description | The novel coronavirus disease, or COVID-19, is a hazardous disease. It is endangering the lives of many people living in more than two hundred countries. It directly affects the lungs. In general, two main imaging modalities, i.e., computed tomography (CT) and chest x-ray (CXR) are used to achieve a speedy and reliable medical diagnosis. Identifying the coronavirus in medical images is exceedingly difficult for diagnosis, assessment, and treatment. It is demanding, time-consuming, and subject to human mistakes. In biological disciplines, excellent performance can be achieved by employing artificial intelligence (AI) models. As a subfield of AI, deep learning (DL) networks have drawn considerable attention than standard machine learning (ML) methods. DL models automatically carry out all the steps of feature extraction, feature selection, and classification. This study has performed comprehensive analysis of coronavirus classification using CXR and CT imaging modalities using DL architectures. Additionally, we have discussed how transfer learning is helpful in this regard. Finally, the problem of designing and implementing a system using computer-aided diagnostic (CAD) to find COVID-19 using DL approaches highlighted a future research possibility. |
| first_indexed | 2024-03-13T01:19:54Z |
| format | Article |
| id | doaj.art-e176364227b74996aae1207fcad8ae41 |
| institution | Directory Open Access Journal |
| issn | 2666-7649 |
| language | English |
| last_indexed | 2024-03-13T01:19:54Z |
| publishDate | 2023-06-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Data Science and Management |
| spelling | doaj.art-e176364227b74996aae1207fcad8ae412023-07-05T05:17:17ZengKeAi Communications Co. Ltd.Data Science and Management2666-76492023-06-016298109Challenges, opportunities, and advances related to COVID-19 classification based on deep learningAbhishek Agnihotri0Narendra Kohli1Corresponding author.; Department of Computer Science and Engineering, Harcourt Butler Technical University, Kanpur, 208002, IndiaDepartment of Computer Science and Engineering, Harcourt Butler Technical University, Kanpur, 208002, IndiaThe novel coronavirus disease, or COVID-19, is a hazardous disease. It is endangering the lives of many people living in more than two hundred countries. It directly affects the lungs. In general, two main imaging modalities, i.e., computed tomography (CT) and chest x-ray (CXR) are used to achieve a speedy and reliable medical diagnosis. Identifying the coronavirus in medical images is exceedingly difficult for diagnosis, assessment, and treatment. It is demanding, time-consuming, and subject to human mistakes. In biological disciplines, excellent performance can be achieved by employing artificial intelligence (AI) models. As a subfield of AI, deep learning (DL) networks have drawn considerable attention than standard machine learning (ML) methods. DL models automatically carry out all the steps of feature extraction, feature selection, and classification. This study has performed comprehensive analysis of coronavirus classification using CXR and CT imaging modalities using DL architectures. Additionally, we have discussed how transfer learning is helpful in this regard. Finally, the problem of designing and implementing a system using computer-aided diagnostic (CAD) to find COVID-19 using DL approaches highlighted a future research possibility.http://www.sciencedirect.com/science/article/pii/S2666764923000188ClassificationCOVID-19CoronavirusDeep learningCAD system |
| spellingShingle | Abhishek Agnihotri Narendra Kohli Challenges, opportunities, and advances related to COVID-19 classification based on deep learning Data Science and Management Classification COVID-19 Coronavirus Deep learning CAD system |
| title | Challenges, opportunities, and advances related to COVID-19 classification based on deep learning |
| title_full | Challenges, opportunities, and advances related to COVID-19 classification based on deep learning |
| title_fullStr | Challenges, opportunities, and advances related to COVID-19 classification based on deep learning |
| title_full_unstemmed | Challenges, opportunities, and advances related to COVID-19 classification based on deep learning |
| title_short | Challenges, opportunities, and advances related to COVID-19 classification based on deep learning |
| title_sort | challenges opportunities and advances related to covid 19 classification based on deep learning |
| topic | Classification COVID-19 Coronavirus Deep learning CAD system |
| url | http://www.sciencedirect.com/science/article/pii/S2666764923000188 |
| work_keys_str_mv | AT abhishekagnihotri challengesopportunitiesandadvancesrelatedtocovid19classificationbasedondeeplearning AT narendrakohli challengesopportunitiesandadvancesrelatedtocovid19classificationbasedondeeplearning |