Transfer Learning-Based Deep Learning Models for Screening Covid-19 Infection from Chest CT Images

5b8560a0-3d6f-478f-8c5b-feea0ca8996820220519045658490naun:naunmdt@crossref.orgMDT DepositInternational Journal of Communications1998-448010.46300/9107http://www.naun.org/cms.action?id=30333520223520221610.46300/9107.2022.16https://npublications.com/journals/communications/2022.phpTransfer Learning-Based Deep Learning Models for Screening Covid-19 Infection from Chest CT ImagesDr. S.MalligaKongu Engineering College, Department of Computer Science and Engineering Perundurai, Erode, Tamil Nadu, IndiaDr. S. V.KogilavaniKongu Engineering College, Department of Computer Science and Engineering Perundurai,Erode, Tamil Nadu, IndiaR.DeeptiKongu Engineering College, Department of Computer Science and Engineering Perundurai, Erode, Tamil Nadu, IndiaS. GowthamKrishnanKongu Engineering College, Department of Computer Science and Engineering Perundurai, Erode, Tamil Nadu, IndiaG. J.AdhithiyaKongu Engineering College, Department of Computer Science and Engineering Perundurai, Erode, Tamil Nadu, IndiaAs the global prevalence of Covid-19 rises, accurate diagnosis of Covid-19 patients is critical. The biggest issue in diagnosing people who test positive is the non-availability or scarcity of testing kits, as Covid-19 spreads rapidly in the community. To prevent Covid-19 from spreading among humans as an alternative quick diagnostic method, an automatic detection system is required. We propose in this study to employ Convolution Neural Networks to detect corona virus-infected patients using Computed Tomography (CT) images. In addition, we look into the transfer learning of deep convolutional neural networks like VGG16, inceptionV3, and Xception for detecting infection in CT scans.To find the best values for hyper-parameters, we use Bayesian optimization. The study comprises of comparing and analysing the employed pre-trained CNN models. According to the data, all trained models are more than 93 percent correct. Pretrained models such as VGG16, InceptionV3, and Xception have attained more than 97 percent precision. Furthermore, our method introduces novel methods for classifying CT scans in order to detect the Covid-19 infection.5172022517202232447https://npublications.com/journals/communications/2022/a142006-007(2022).pdf10.46300/9107.2022.16.7https://npublications.com/journals/communications/2022/a142006-007(2022).pdf10.1148/radiol.2020200236J. Lei, J. Li, X. Li, X. Qi, "CT Imaging of the 2019 Novel Coronavirus Pneumonia", Radiology, vol. 295, no. 1, Jan.2020, pp. 18-18. 10.1148/radiol.2020200269H. Shi, X. Han, C. Zheng, "Evolution of CT Manifestations in a Patient Recovered from 2019 Novel Coronavirus Pneumonia in Wuhan, China", Radiology, vol. 295, Feb 2020, pp. 20-20. 10.1007/978-3-030-01264-9_26Z. Yang, T. Luo, D. Wang, Z. Hu, J. Gao, L. Wang, "Learning to Navigate for Fine-Grained Classification", In Proc. 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