@article{scholars17700,
             doi = {10.1007/978-3-030-96308-8{$_8$}{$_7$}},
          volume = {418 LN},
           title = {COVID Detection Using Chest X-Ray and Transfer Learning},
           pages = {933--943},
            note = {cited By 29; Conference of 21st International Conference on Intelligent Systems Design and Applications, ISDA 2021 ; Conference Date: 13 December 2021 Through 15 December 2021; Conference Code:275899},
         journal = {Lecture Notes in Networks and Systems},
            year = {2022},
       publisher = {Springer Science and Business Media Deutschland GmbH},
            issn = {23673370},
            isbn = {9783030963071},
          author = {Jain, S. and Sindhwani, N. and Anand, R. and Kannan, R.},
        abstract = {As per World Health Organization, COVID-19 is causing even the most important health systems across the countries under considerable strain. The advanced recognition of COVID 19 will result into decreasing the stress of a lot of health systems. Much similar to the customary usage of Chest X-Rays for detecting different pathologies, COVID-19 can also be detected using X-Ray of patients that indicates a very critical function in the diagnosis of SARS Covid-19. With rampant growth in the area of Deep Learning (DL) as well as Machine Learning (ML), it is much easier to design the framework that can detect COVID-19 infection easily. This paper proposes deep learning-based detection process by incorporating the concept of Transfer Learning for the classification of this pandemic using X-ray images of chest. This non-invasive and early-prediction of the corona virus by observing the X-rays of chest can subsequently be utilized to estimate the expansion of COVID-19 in the patients. This study got a maximum of 97 classifiers{\^a}?? accuracy using ResNet based model. This method can be utilized to upscale the effectiveness of the screening process. {\^A}{\copyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127672464&doi=10.1007\%2f978-3-030-96308-8\%5f87&partnerID=40&md5=00f52e3cc1bf92f1756740b882e2905e}
}