Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images
COVID-19 has sparked a global pandemic, with a variety of inflamed instances and deaths increasing on an everyday basis. Researchers are actively increasing and improving distinct mathematical and ML algorithms to forecast the infection. The prediction and detection of the Omicron variant of COVID-1...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Interdisciplinary Perspectives on Infectious Diseases |
| Online Access: | http://dx.doi.org/10.1155/2022/1525615 |
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| author | Anand Kumar Gupta Asadi Srinivasulu Kamal Kant Hiran Goddindla Sreenivasulu Sivaram Rajeyyagari Madhusudhana Subramanyam |
| author_facet | Anand Kumar Gupta Asadi Srinivasulu Kamal Kant Hiran Goddindla Sreenivasulu Sivaram Rajeyyagari Madhusudhana Subramanyam |
| author_sort | Anand Kumar Gupta |
| collection | DOAJ |
| description | COVID-19 has sparked a global pandemic, with a variety of inflamed instances and deaths increasing on an everyday basis. Researchers are actively increasing and improving distinct mathematical and ML algorithms to forecast the infection. The prediction and detection of the Omicron variant of COVID-19 brought new issues for the health fraternity due to its ubiquity in human beings. In this research work, two learning algorithms, namely, deep learning (DL) and machine learning (ML), were developed to forecast the Omicron virus infections. Automatic disease prediction and detection have become crucial issues in medical science due to rapid population growth. In this research study, a combined Extended CNN-RNN research model was developed on a chest CT-scan image dataset to predict the number of +ve and −ve cases of Omicron virus infections. The proposed research model was evaluated and compared against the existing system utilizing a dataset of 16,733-sample training and testing CT-scan images collected from the Kaggle repository. This research article aims to introduce a combined ML and DL technique based on the combination of an Extended Convolutional Neural Network (ECNN) and an Extended Recurrent Neural Network (ERNN) to diagnose and predict Omicron virus-infected cases automatically using chest CT-scan images. To overcome the drawbacks of the existing system, this research proposes a combined research model that is ECNN-ERNN, where ECNN is used for the extraction of deep features and ERNN is used for exploration using extracted features. A dataset of 16,733 Omicron computer tomography images was used as a pilot assessment for this proposed prototype. The investigational experiment results show that the projected prototype provides 97.50% accuracy, 98.10% specificity, 98.80% of AUC, and 97.70% of F1-score. To the last, the study outlines the advantages being offered by the proposed model with respect to other existing models by comparing different parameters of validation such as accuracy, error rate, data size, time complexity, and execution time. |
| format | Article |
| id | doaj-art-db7c6fa316ba4dccb5a49efb7ab5c7ca |
| institution | Kabale University |
| issn | 1687-7098 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Interdisciplinary Perspectives on Infectious Diseases |
| spelling | doaj-art-db7c6fa316ba4dccb5a49efb7ab5c7ca2025-08-20T03:24:17ZengWileyInterdisciplinary Perspectives on Infectious Diseases1687-70982022-01-01202210.1155/2022/1525615Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan ImagesAnand Kumar Gupta0Asadi Srinivasulu1Kamal Kant Hiran2Goddindla Sreenivasulu3Sivaram Rajeyyagari4Madhusudhana Subramanyam5Data Science Research LaboratoryData Science Research LaboratorySymbiosis University of Applied SciencesDepartment of Chemical EngineeringDepartment of CSEDepartment of Computer Science and EngineeringCOVID-19 has sparked a global pandemic, with a variety of inflamed instances and deaths increasing on an everyday basis. Researchers are actively increasing and improving distinct mathematical and ML algorithms to forecast the infection. The prediction and detection of the Omicron variant of COVID-19 brought new issues for the health fraternity due to its ubiquity in human beings. In this research work, two learning algorithms, namely, deep learning (DL) and machine learning (ML), were developed to forecast the Omicron virus infections. Automatic disease prediction and detection have become crucial issues in medical science due to rapid population growth. In this research study, a combined Extended CNN-RNN research model was developed on a chest CT-scan image dataset to predict the number of +ve and −ve cases of Omicron virus infections. The proposed research model was evaluated and compared against the existing system utilizing a dataset of 16,733-sample training and testing CT-scan images collected from the Kaggle repository. This research article aims to introduce a combined ML and DL technique based on the combination of an Extended Convolutional Neural Network (ECNN) and an Extended Recurrent Neural Network (ERNN) to diagnose and predict Omicron virus-infected cases automatically using chest CT-scan images. To overcome the drawbacks of the existing system, this research proposes a combined research model that is ECNN-ERNN, where ECNN is used for the extraction of deep features and ERNN is used for exploration using extracted features. A dataset of 16,733 Omicron computer tomography images was used as a pilot assessment for this proposed prototype. The investigational experiment results show that the projected prototype provides 97.50% accuracy, 98.10% specificity, 98.80% of AUC, and 97.70% of F1-score. To the last, the study outlines the advantages being offered by the proposed model with respect to other existing models by comparing different parameters of validation such as accuracy, error rate, data size, time complexity, and execution time.http://dx.doi.org/10.1155/2022/1525615 |
| spellingShingle | Anand Kumar Gupta Asadi Srinivasulu Kamal Kant Hiran Goddindla Sreenivasulu Sivaram Rajeyyagari Madhusudhana Subramanyam Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images Interdisciplinary Perspectives on Infectious Diseases |
| title | Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images |
| title_full | Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images |
| title_fullStr | Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images |
| title_full_unstemmed | Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images |
| title_short | Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images |
| title_sort | prediction of omicron virus using combined extended convolutional and recurrent neural networks technique on ct scan images |
| url | http://dx.doi.org/10.1155/2022/1525615 |
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