Classification of Lung Images of COVID-19 Patients With the Application of Deep Learning Techniques:

C. Meenakshi Vels Institute of Science, Technology, Advanced Studies, India https://orcid.org/0000-0002-9020-6031 S. Meyyappan MIT Campus, Anna University, Chennai, India A. Ganesh Ram Anna University, India M. Vijayakarthick Anna University, India https://orcid.org/0000-0002-8000-3062 N. Vinoth Anna University, India Bhopendra Singh Amity University, Dubai, UAE Classification of Lung Images of COVID-19 Patients With the Application of Deep Learning Techniques

This study introduces a smart approach that uses deep learning and feature extraction from chest CT scans to detect COVID-19 quickly and accurately. Strategically integrating transfer learning with pre-trained models to improve COVID-19 diagnosis is the major innovation. Two key phases comprise the research approach. Transfer learning is first used to deep learning models using CNNs like MobileNet, DenseNet, Xception, ResNet, InceptionV3, InceptionResNetV2, VGGNet, and NASNet. PCA is used to improve feature representation and classification accuracy in these models after extensive training, testing, and validation. Kapur's entropy thresholding, morphology-based segmentation, and k-means clustering, enriched by transfer learning paradigms, are used for feature extraction. High-quality features are extracted using these methods, improving CT picture interpretability and informativeness. The results reveal that this integrative strategy improves detection accuracy, sensitivity, specificity, and performance.
chapter 5 2024 4 26 66 79 10.4018/979-8-3693-5946-4.ch005 20240501110912 https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-5946-4.ch005 https://www.igi-global.com/viewtitle.aspx?TitleId=346191 10.1007/s00521-021-06344-5 10.1007/s10489-020-01826-w 10.1007/s10044-020-00950-0 10.2174/1573405616666200604163954 10.7717/peerj-cs.495 10.32604/cmc.2021.014134 10.1101/2020.04.24.20078584 10.1101/2020.04.24.20078584 10.1109/TENCON50793.2020.9293887 Ankan Ghosh Dastider, M. R., Subah, F., Sadik, T., & Mahmud, S. A. (2020). ResCovNet: A Deep Learning-Based Architecture For COVID-19 Detection From Chest CT Scan Images. In 2020 IEEE REGION 10 CONFERENCE (TENCON), Osaka, Japan. 10.1016/j.compbiomed.2021.104575 10.1128/CMR.00102-14 10.1128/CMR.00023-07 10.21203/rs.3.rs-65954/v2 Rapid AI development cycle for the Coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis. In arXiv O.Gozes 2020 GozesO.Frid-AdarM.GreenspanH.BrowningP. D.ZhangH.JiW.BernheimA.SiegelE. (2020). Rapid AI development cycle for the Coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis. In arXiv[cs.CV]. http://arxiv.org/abs/2003.05037 10.1088/2632-2153/abf22c 10.1101/2020.04.13.20063941 10.1016/S0140-6736(20)30183-5 10.1080/07391102.2020.1788642 10.1101/2020.02.20.20025536 10.20944/preprints201909.0139.v1 10.1056/NEJMp2000929 10.1007/s10044-021-00984-y 10.1109/TNNLS.2021.3054746 10.1016/j.irbm.2020.05.003 10.1016/j.compbiomed.2021.104835 Transfusion: Understanding transfer learning for medical imaging. In arXiv M.Raghu 2019 RaghuM.ZhangC.KleinbergJ.BengioS. (2019). Transfusion: Understanding transfer learning for medical imaging. In arXiv[cs.CV]. http://arxiv.org/abs/1902.07208 10.3390/sym12071146 10.1016/j.compbiomed.2021.104306 10.1109/RBME.2020.2987975 Automated detection and forecasting of COVID-19 using deep learning techniques: A review. In arXiv A.Shoeibi 2020 ShoeibiA.KhodatarsM.JafariM.GhassemiN.SadeghiD.MoridianP.KhademA.AlizadehsaniR.HussainS.ZareA.SaniZ. A.KhozeimehF.NahavandiS.AcharyaU. R.GorrizJ. M. (2020). Automated detection and forecasting of COVID-19 using deep learning techniques: A review. In arXiv[cs.LG]. http://arxiv.org/abs/2007.10785 10.1109/TII.2020.3048391 10.1109/ACCESS.2020.3027685 10.3390/e23020204 10.1038/s41598-020-76550-z 10.1101/2020.03.12.20027185 10.1016/j.asoc.2020.106885 10.1038/nrd.2015.37