RETRACTED ARTICLE: Conceptualising a channel-based overlapping CNN tower architecture for COVID-19 identification from CT-scan images
Abstract Convolutional Neural Network (CNN) has been employed in classifying the COVID cases from the lungs’ CT-Scan with promising quantifying metrics. However, SARS COVID-19 has been mutated, and we have many versions of the virus B.1.1.7, B.1.135, and P.1, hence there is a need for a more robust...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-21700-8 |
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| author | Ravi Shekhar Tiwari Lakshmi D Tapan Kumar Das Kathiravan Srinivasan Chuan-Yu Chang |
| author_facet | Ravi Shekhar Tiwari Lakshmi D Tapan Kumar Das Kathiravan Srinivasan Chuan-Yu Chang |
| author_sort | Ravi Shekhar Tiwari |
| collection | DOAJ |
| description | Abstract Convolutional Neural Network (CNN) has been employed in classifying the COVID cases from the lungs’ CT-Scan with promising quantifying metrics. However, SARS COVID-19 has been mutated, and we have many versions of the virus B.1.1.7, B.1.135, and P.1, hence there is a need for a more robust architecture that will classify the COVID positive patients from COVID negative patients with less training. We have developed a neural network based on the number of channels present in the images. The CNN architecture is developed in accordance with the number of the channels present in the dataset and are extracting the features separately from the channels present in the CT-Scan dataset. In the tower architecture, the first tower is dedicated for only the first channel present in the image; the second CNN tower is dedicated to the first and second channel feature maps, and finally the third channel takes account of all the feature maps from all three channels. We have used two datasets viz. one from Tongji Hospital, Wuhan, China and another SARS-CoV-2 dataset to train and evaluate our CNN architecture. The proposed model brought about an average accuracy of 99.4%, F1 score 0.988, and AUC 0.99. |
| format | Article |
| id | doaj-art-5a9cd52f06cb45088653d8c3299e0251 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2022-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-5a9cd52f06cb45088653d8c3299e02512025-01-19T12:25:02ZengNature PortfolioScientific Reports2045-23222022-10-0112111510.1038/s41598-022-21700-8RETRACTED ARTICLE: Conceptualising a channel-based overlapping CNN tower architecture for COVID-19 identification from CT-scan imagesRavi Shekhar Tiwari0Lakshmi D1Tapan Kumar Das2Kathiravan Srinivasan3Chuan-Yu Chang4Artificial Intelligence Engineering, Chadura TechSchool of Computing Science and Engineering, VIT Bhopal UniversitySchool of Information Technology and Engineering, Vellore Institute of TechnologySchool of Computer Science and Engineering, Vellore Institute of TechnologyDepartment of Computer Science and Information Engineering, National Yunlin University of Science and Technology, YunlinAbstract Convolutional Neural Network (CNN) has been employed in classifying the COVID cases from the lungs’ CT-Scan with promising quantifying metrics. However, SARS COVID-19 has been mutated, and we have many versions of the virus B.1.1.7, B.1.135, and P.1, hence there is a need for a more robust architecture that will classify the COVID positive patients from COVID negative patients with less training. We have developed a neural network based on the number of channels present in the images. The CNN architecture is developed in accordance with the number of the channels present in the dataset and are extracting the features separately from the channels present in the CT-Scan dataset. In the tower architecture, the first tower is dedicated for only the first channel present in the image; the second CNN tower is dedicated to the first and second channel feature maps, and finally the third channel takes account of all the feature maps from all three channels. We have used two datasets viz. one from Tongji Hospital, Wuhan, China and another SARS-CoV-2 dataset to train and evaluate our CNN architecture. The proposed model brought about an average accuracy of 99.4%, F1 score 0.988, and AUC 0.99.https://doi.org/10.1038/s41598-022-21700-8 |
| spellingShingle | Ravi Shekhar Tiwari Lakshmi D Tapan Kumar Das Kathiravan Srinivasan Chuan-Yu Chang RETRACTED ARTICLE: Conceptualising a channel-based overlapping CNN tower architecture for COVID-19 identification from CT-scan images Scientific Reports |
| title | RETRACTED ARTICLE: Conceptualising a channel-based overlapping CNN tower architecture for COVID-19 identification from CT-scan images |
| title_full | RETRACTED ARTICLE: Conceptualising a channel-based overlapping CNN tower architecture for COVID-19 identification from CT-scan images |
| title_fullStr | RETRACTED ARTICLE: Conceptualising a channel-based overlapping CNN tower architecture for COVID-19 identification from CT-scan images |
| title_full_unstemmed | RETRACTED ARTICLE: Conceptualising a channel-based overlapping CNN tower architecture for COVID-19 identification from CT-scan images |
| title_short | RETRACTED ARTICLE: Conceptualising a channel-based overlapping CNN tower architecture for COVID-19 identification from CT-scan images |
| title_sort | retracted article conceptualising a channel based overlapping cnn tower architecture for covid 19 identification from ct scan images |
| url | https://doi.org/10.1038/s41598-022-21700-8 |
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