Multiclass Supervised Learning Approach for SAR-COV2 Severity and Scope Prediction: SC2SSP Framework
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Abstract
Purpose: Identifying high-risk areas for the virus or the potential for the technique to be applied to this infectious disease might be difficult. The existing tools being used for predicting viruses exhibit various limitations. The severe pneumonia caused by the rapidly spreading coronavirus disease (COVID-19) is predicted to have a significant negative impact on the healthcare sector. Accurate treatment requires an urgent need for early diagnosis, which reduces pressure on the healthcare system. Computed Tomography (CT) scan and Chest X-Ray (CXR) are some of the standard image diagnoses. Although a CT scan is the most common method for diagnosis, CXR is the most frequently utilized since it is more accessible, quicker, and less expensive.
Materials and Methods: In this manuscript, the proposed model SC2SSP is a multiclass supervised learning technique that aims to predict the scope and severity of the SAR-COV2 virus using data on confirmed cases and deaths. The model may also utilize preprocessing techniques which are Gaussian smoothing for handling imbalanced data, such as oversampling or under sampling, as well as feature extraction methods such as Local Binary Pattern to identify the most relevant input features for the prediction task. Additionally, a classifier such as XGBoost can also be used to further improve the model's performance. This makes the model more robust and accurate in predicting the scope and severity of the SAR-COV2 virus.
Results: The model utilizes the Exact Greedy Algorithm to classify the spread and impact of the virus in different regions. The performance metrics like accuracy, precision, fscore and sensitivity are analyzing the proposed method performance. The proposed SC2SSP approach attains 3.101% and 7.12% higher accuracy; 24.13% and 13.04% higher precision compared with existing methods, like the Detection of COVID-19 from Chest X-ray Images Using Convolutional Neural Networks (Resnet50), Deep learning for automated recognition of covid-19 from chest X-ray images (VGGNet), respectively.
Conclusion: The conclusion and potential future healthcare planning follow the exploration of evidence-based approaches and modalities in the scope and forecast.
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2- Turkoglu, M. “COVID-19 detection system using chest CT images and multiple kernels-extreme learning machine based on deep neural network.” Irbm, 42(4), pp.207-214, (2021).
3- Shajin, F.H., Rajesh, P., Raja, M.R. “An efficient VLSI architecture for fast motion estimation exploiting zero motion prejudgment technique and a new quadrant-based search algorithm in HEVC.” Circuits, Systems, and Signal Processing, pp.1-24, (2022).
4- Rajesh, P., Shajin, F.H., Kumaran, G.K. “An Efficient IWOLRS Control Technique of Brushless DC Motor for Torque Ripple Minimization.” Applied Science and Engineering Progress, 15(3), pp.5514-5514, (2022).
5- Shajin, F.H., Rajesh, P., Nagoji Rao, V.K. “Efficient Framework for Brain Tumour Classification using Hierarchical Deep Learning Neural Network Classifier.” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, pp.1-8, (2022).
6- Rajesh, P., Shajin, F.H., Kannayeram, G. “A novel intelligent technique for energy management in smart home using internet of things.” Applied Soft Computing, 128, p.109442, (2022).
7- Han, S., Roy, P.K., Hossain, M.I., Byun, K.H., Choi, C., Ha, S.D. “COVID-19 pandemic crisis and food safety: Implications and inactivation strategies.” Trends in Food Science & Technology, 109, pp.25-36, (2021).
8- Qiblawey, Y., Tahir, A., Chowdhury, M.E., Khandakar, A., Kiranyaz, S., Rahman, T., Ibtehaz, N., Mahmud, S., Maadeed, S.A., Musharavati, F., Ayari, M.A. “Detection and severity classification of COVID-19 in CT images using deep learning.” Diagnostics, 11(5), p.893, (2021).
9- Fung, D.L., Liu, Q., Zammit, J., Leung, C.K.S., Hu, P. “Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19.” Journal of Translational Medicine, 19, pp.1-18, (2021).
10- Sekeroglu, B., Ozsahin, I. “ Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks.” SLAS TECHNOLOGY: Translating Life Sciences Innovation, 25(6), pp.553-565, (2020).
11- Rezaeijo, S.M., Ghorvei, M., Abedi-Firouzjah, R., Mojtahedi, H., Entezari Zarch, H. “Detecting COVID-19 in chest images based on deep transfer learning and machine learning algorithms.” Egyptian Journal of Radiology and Nuclear Medicine, 52(1), pp.1-12, (2021).
12- Li, Y., Jiang, Y., Gu, Y., Qian, P. “An Automatic Detection Method for COVID-19 in CT Images.” In Journal of Physics: Conference Series (Vol. 2278, No. 1, p. 012044). IOP Publishing. (2022).
13- Ibrahim, M.R., Youssef, S.M., Fathalla, K.M. “Abnormality detection and intelligent severity assessment of human chest computed tomography scans using deep learning: a case study on SARS-COV-2 assessment.” Journal of Ambient Intelligence and Humanized Computing, pp.1-24, (2021).
14- Cifci, M.A. “Deep learning model for diagnosis of corona virus disease from CT images.” Int. J. Sci. Eng. Res, 11(4), pp.273-278, (2020).
15- Irmak, E. “COVID‐19 disease severity assessment using CNN model.” IET Image Processing, 15(8), pp.1814-1824, (2021).
16- Rahman, T., Akinbi, A., Chowdhury, M.E., Rashid, T.A., Şengür, A., Khandakar, A., Islam, K.R., Ismael, A.M. “COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network.” Health Information Science and Systems, 10(1), p.1, (2022).
17- Alghamdi, H.S., Amoudi, G., Elhag, S., Saeedi, K., Nasser, J. “Deep learning approaches for detecting COVID-19 from chest X-ray images: A survey.” Ieee Access, 9, pp.20235-20254, (2021).
18- AKGÜN, D., KABAKUŞ, A.T., ŞENTÜRK, Z.K., ŞENTÜRK, A., KÜÇÜKKÜLAHLI, E. “A transfer learning-based deep learning approach for automated COVID-19diagnosis with audio data.” Turkish Journal of Electrical Engineering and Computer Sciences, 29(8), pp.2807-2823, (2021).
19- Chattopadhyay, S. “Towards grading chest X-rays of COVID-19 patients using a dynamic radial basis function network classifier.” Artificial Intelligence Evolution, pp.81-95, (2021).
20- Tayarani, M. “Applications of artificial intelligence in battling against covid-19: A literature review.” Chaos, Solitons & Fractals. (2020).
21- Vasal, S., Jain, S., Verma, A. “COVID-AI: an artificial intelligence system to diagnose COVID 19 disease.” J Eng Res Technol, 9, pp.1-6, (2020).
22- Bouchareb, Y., Khaniabadi, P.M., Al Kindi, F., Al Dhuhli, H., Shiri, I., Zaidi, H., Rahmim, A. “Artificial intelligence-driven assessment of radiological images for COVID-19.” Computers in Biology and Medicine, 136, p.104665, (2021).
23- Shah, P.M., Ullah, F., Shah, D., Gani, A., Maple, C., Wang, Y., Abrar, M., Islam, S.U. “Deep GRU-CNN model for COVID-19 detection from chest X-rays data.” Ieee Access, 10, pp.35094-35105, (2021).
24- Park, S., Kim, G., Kim, J., Kim, B., Ye, J.C. “Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training.” arXiv preprint arXiv:2111.01338, (2021).
25- Verma, A.K., Vamsi, I., Saurabh, P., Sudha, R., Sabareesh, G.R., Rajkumar, S. “Wavelet and deep learning-based detection of SARS-nCoV from thoracic X-ray images for rapid and efficient testing.” Expert Systems with Applications, 185, p.115650, (2021).
26- Li, X., Geng, M., Peng, Y., Meng, L., Lu, S. “Molecular immune pathogenesis and diagnosis of COVID-19.” Journal of Pharmaceutical Analysis, 10(2), pp.102-108, (2020).
27- Wang, L., Lin, Z.Q., Wong, A. “Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images.” Scientific Reports, 10(1), pp.1-12, (2020).
28- Borakati, A., Perera, A., Johnson, J., Sood, T. “Diagnostic accuracy of X-ray versus CT in COVID-19: a propensity-matched database study.” BMJ Open, 10(11), p.e042946, (2020).
29- Loey, M., Manogaran, G., Khalifa, N.E.M. “A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images.” Neural Computing and Applications, pp.1-13, (2020).
30- Zheng, Z., Peng, F., Xu, B., Zhao, J., Liu, H., Peng, J., Li, Q., Jiang, C., Zhou, Y., Liu, S., Ye, C. “Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis.” Journal of Infection, 81(2), pp.e16-e25, (2020).
31- Wang, X., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., Zheng, C. “A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT.” IEEE Transactions on Medical Imaging, 39(8), pp.2615-2625, (2020).
32- Pathak, Y., Shukla, P.K., Tiwari, A., Stalin, S., Singh, S. “Deep transfer learning based classification model for COVID-19 disease.” Irbm, 43(2), pp.87-92, (2022).
33- Han, Z., Wei, B., Hong, Y., Li, T., Cong, J., Zhu, X., Wei, H., Zhang, W. “Accurate screening of COVID-19 using attention-based deep 3D multiple instance learning.” IEEE Transactions on Medical Imaging, 39(8), pp.2584-2594, (2020).
34- Ozsahin, I., Sekeroglu, B., Musa, M.S., Mustapha, M.T., Ozsahin, D.U. “Review on diagnosis of COVID-19 from chest CT images using artificial intelligence.” Computational and Mathematical Methods in Medicine, 2020, (2020).
35- Jin, C., Chen, W., Cao, Y., Xu, Z., Tan, Z., Zhang, X., Deng, L., Zheng, C., Zhou, J., Shi, H., Feng, J. “Development and evaluation of an artificial intelligence system for COVID-19 diagnosis.” Nature Communications, 11(1), p.5088, (2020).
36- Ahuja, S., Panigrahi, B.K., Dey, N., Rajinikanth, V., Gandhi, T.K. “Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices.” Applied Intelligence, 51, pp.571-585, (2021).
37- Xiao, L.S., Li, P., Sun, F., Zhang, Y., Xu, C., Zhu, H., Cai, F.Q., He, Y.L., Zhang, W.F., Ma, S.C., Hu, C. “Development and validation of a deep learning-based model using computed tomography imaging for predicting disease severity of coronavirus disease 2019.” Frontiers in Bioengineering and Biotechnology, 8, p.898, (2020).
38- Pu, J., Leader, J.K., Bandos, A., Ke, S., Wang, J., Shi, J., Du, P., Guo, Y., Wenzel, S.E., Fuhrman, C.R., Wilson, D.O. “Automated quantification of COVID-19 severity and progression using chest CT images.” European Radiology, 31, pp.436-446, (2021).
39- Shen, C., Yu, N., Cai, S., Zhou, J., Sheng, J., Liu, K., Zhou, H., Guo, Y., Niu, G. “Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019.” Journal of Pharmaceutical Analysis, 10(2), pp.123-129, (2020).
40- Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., Xue, Z., Shen, D., Shi, Y. “Abnormal lung quantification in chest CT images of COVID‐19 patients with deep learning and its application to severity prediction.” Medical Physics, 48(4), pp.1633-1645, (2021).
2- Turkoglu, M. “COVID-19 detection system using chest CT images and multiple kernels-extreme learning machine based on deep neural network.” Irbm, 42(4), pp.207-214, (2021).
3- Shajin, F.H., Rajesh, P., Raja, M.R. “An efficient VLSI architecture for fast motion estimation exploiting zero motion prejudgment technique and a new quadrant-based search algorithm in HEVC.” Circuits, Systems, and Signal Processing, pp.1-24, (2022).
4- Rajesh, P., Shajin, F.H., Kumaran, G.K. “An Efficient IWOLRS Control Technique of Brushless DC Motor for Torque Ripple Minimization.” Applied Science and Engineering Progress, 15(3), pp.5514-5514, (2022).
5- Shajin, F.H., Rajesh, P., Nagoji Rao, V.K. “Efficient Framework for Brain Tumour Classification using Hierarchical Deep Learning Neural Network Classifier.” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, pp.1-8, (2022).
6- Rajesh, P., Shajin, F.H., Kannayeram, G. “A novel intelligent technique for energy management in smart home using internet of things.” Applied Soft Computing, 128, p.109442, (2022).
7- Han, S., Roy, P.K., Hossain, M.I., Byun, K.H., Choi, C., Ha, S.D. “COVID-19 pandemic crisis and food safety: Implications and inactivation strategies.” Trends in Food Science & Technology, 109, pp.25-36, (2021).
8- Qiblawey, Y., Tahir, A., Chowdhury, M.E., Khandakar, A., Kiranyaz, S., Rahman, T., Ibtehaz, N., Mahmud, S., Maadeed, S.A., Musharavati, F., Ayari, M.A. “Detection and severity classification of COVID-19 in CT images using deep learning.” Diagnostics, 11(5), p.893, (2021).
9- Fung, D.L., Liu, Q., Zammit, J., Leung, C.K.S., Hu, P. “Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19.” Journal of Translational Medicine, 19, pp.1-18, (2021).
10- Sekeroglu, B., Ozsahin, I. “ Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks.” SLAS TECHNOLOGY: Translating Life Sciences Innovation, 25(6), pp.553-565, (2020).
11- Rezaeijo, S.M., Ghorvei, M., Abedi-Firouzjah, R., Mojtahedi, H., Entezari Zarch, H. “Detecting COVID-19 in chest images based on deep transfer learning and machine learning algorithms.” Egyptian Journal of Radiology and Nuclear Medicine, 52(1), pp.1-12, (2021).
12- Li, Y., Jiang, Y., Gu, Y., Qian, P. “An Automatic Detection Method for COVID-19 in CT Images.” In Journal of Physics: Conference Series (Vol. 2278, No. 1, p. 012044). IOP Publishing. (2022).
13- Ibrahim, M.R., Youssef, S.M., Fathalla, K.M. “Abnormality detection and intelligent severity assessment of human chest computed tomography scans using deep learning: a case study on SARS-COV-2 assessment.” Journal of Ambient Intelligence and Humanized Computing, pp.1-24, (2021).
14- Cifci, M.A. “Deep learning model for diagnosis of corona virus disease from CT images.” Int. J. Sci. Eng. Res, 11(4), pp.273-278, (2020).
15- Irmak, E. “COVID‐19 disease severity assessment using CNN model.” IET Image Processing, 15(8), pp.1814-1824, (2021).
16- Rahman, T., Akinbi, A., Chowdhury, M.E., Rashid, T.A., Şengür, A., Khandakar, A., Islam, K.R., Ismael, A.M. “COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network.” Health Information Science and Systems, 10(1), p.1, (2022).
17- Alghamdi, H.S., Amoudi, G., Elhag, S., Saeedi, K., Nasser, J. “Deep learning approaches for detecting COVID-19 from chest X-ray images: A survey.” Ieee Access, 9, pp.20235-20254, (2021).
18- AKGÜN, D., KABAKUŞ, A.T., ŞENTÜRK, Z.K., ŞENTÜRK, A., KÜÇÜKKÜLAHLI, E. “A transfer learning-based deep learning approach for automated COVID-19diagnosis with audio data.” Turkish Journal of Electrical Engineering and Computer Sciences, 29(8), pp.2807-2823, (2021).
19- Chattopadhyay, S. “Towards grading chest X-rays of COVID-19 patients using a dynamic radial basis function network classifier.” Artificial Intelligence Evolution, pp.81-95, (2021).
20- Tayarani, M. “Applications of artificial intelligence in battling against covid-19: A literature review.” Chaos, Solitons & Fractals. (2020).
21- Vasal, S., Jain, S., Verma, A. “COVID-AI: an artificial intelligence system to diagnose COVID 19 disease.” J Eng Res Technol, 9, pp.1-6, (2020).
22- Bouchareb, Y., Khaniabadi, P.M., Al Kindi, F., Al Dhuhli, H., Shiri, I., Zaidi, H., Rahmim, A. “Artificial intelligence-driven assessment of radiological images for COVID-19.” Computers in Biology and Medicine, 136, p.104665, (2021).
23- Shah, P.M., Ullah, F., Shah, D., Gani, A., Maple, C., Wang, Y., Abrar, M., Islam, S.U. “Deep GRU-CNN model for COVID-19 detection from chest X-rays data.” Ieee Access, 10, pp.35094-35105, (2021).
24- Park, S., Kim, G., Kim, J., Kim, B., Ye, J.C. “Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training.” arXiv preprint arXiv:2111.01338, (2021).
25- Verma, A.K., Vamsi, I., Saurabh, P., Sudha, R., Sabareesh, G.R., Rajkumar, S. “Wavelet and deep learning-based detection of SARS-nCoV from thoracic X-ray images for rapid and efficient testing.” Expert Systems with Applications, 185, p.115650, (2021).
26- Li, X., Geng, M., Peng, Y., Meng, L., Lu, S. “Molecular immune pathogenesis and diagnosis of COVID-19.” Journal of Pharmaceutical Analysis, 10(2), pp.102-108, (2020).
27- Wang, L., Lin, Z.Q., Wong, A. “Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images.” Scientific Reports, 10(1), pp.1-12, (2020).
28- Borakati, A., Perera, A., Johnson, J., Sood, T. “Diagnostic accuracy of X-ray versus CT in COVID-19: a propensity-matched database study.” BMJ Open, 10(11), p.e042946, (2020).
29- Loey, M., Manogaran, G., Khalifa, N.E.M. “A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images.” Neural Computing and Applications, pp.1-13, (2020).
30- Zheng, Z., Peng, F., Xu, B., Zhao, J., Liu, H., Peng, J., Li, Q., Jiang, C., Zhou, Y., Liu, S., Ye, C. “Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis.” Journal of Infection, 81(2), pp.e16-e25, (2020).
31- Wang, X., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., Zheng, C. “A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT.” IEEE Transactions on Medical Imaging, 39(8), pp.2615-2625, (2020).
32- Pathak, Y., Shukla, P.K., Tiwari, A., Stalin, S., Singh, S. “Deep transfer learning based classification model for COVID-19 disease.” Irbm, 43(2), pp.87-92, (2022).
33- Han, Z., Wei, B., Hong, Y., Li, T., Cong, J., Zhu, X., Wei, H., Zhang, W. “Accurate screening of COVID-19 using attention-based deep 3D multiple instance learning.” IEEE Transactions on Medical Imaging, 39(8), pp.2584-2594, (2020).
34- Ozsahin, I., Sekeroglu, B., Musa, M.S., Mustapha, M.T., Ozsahin, D.U. “Review on diagnosis of COVID-19 from chest CT images using artificial intelligence.” Computational and Mathematical Methods in Medicine, 2020, (2020).
35- Jin, C., Chen, W., Cao, Y., Xu, Z., Tan, Z., Zhang, X., Deng, L., Zheng, C., Zhou, J., Shi, H., Feng, J. “Development and evaluation of an artificial intelligence system for COVID-19 diagnosis.” Nature Communications, 11(1), p.5088, (2020).
36- Ahuja, S., Panigrahi, B.K., Dey, N., Rajinikanth, V., Gandhi, T.K. “Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices.” Applied Intelligence, 51, pp.571-585, (2021).
37- Xiao, L.S., Li, P., Sun, F., Zhang, Y., Xu, C., Zhu, H., Cai, F.Q., He, Y.L., Zhang, W.F., Ma, S.C., Hu, C. “Development and validation of a deep learning-based model using computed tomography imaging for predicting disease severity of coronavirus disease 2019.” Frontiers in Bioengineering and Biotechnology, 8, p.898, (2020).
38- Pu, J., Leader, J.K., Bandos, A., Ke, S., Wang, J., Shi, J., Du, P., Guo, Y., Wenzel, S.E., Fuhrman, C.R., Wilson, D.O. “Automated quantification of COVID-19 severity and progression using chest CT images.” European Radiology, 31, pp.436-446, (2021).
39- Shen, C., Yu, N., Cai, S., Zhou, J., Sheng, J., Liu, K., Zhou, H., Guo, Y., Niu, G. “Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019.” Journal of Pharmaceutical Analysis, 10(2), pp.123-129, (2020).
40- Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., Xue, Z., Shen, D., Shi, Y. “Abnormal lung quantification in chest CT images of COVID‐19 patients with deep learning and its application to severity prediction.” Medical Physics, 48(4), pp.1633-1645, (2021).
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| Issue | Vol 12 No 1 (2025) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v12i1.17732 | |
| Keywords | ||
| Supervised Learning COVID-19 AUC-ROC Deep Learning and Neural Nets | ||
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How to Cite
1.
Saheb SK, Narayanan B, Narayana Rao T. Multiclass Supervised Learning Approach for SAR-COV2 Severity and Scope Prediction: SC2SSP Framework. Frontiers Biomed Technol. 2025;12(1):38-53.
