Bhowmik, B.Varna, S.A.Kumar, A.Kumar, R.2026-02-062021Midwest Symposium on Circuits and Systems, 2021, Vol.2021-August, , p. 404-40715483746https://doi.org/10.1109/MWSCAS47672.2021.9531825https://idr.nitk.ac.in/handle/123456789/30114This paper proposes a custom deep neural network-based scheme for coronavirus disease 2019 (COVID-19) detection. The proposed method takes X-ray images that use transfer learning techniques on pre-trained models. One objective of this work is to quickening the detection of the virus. Another goal is to reduce the number of falsely detected cases by a significant margin. The experimental setup demonstrates promising results on the selected dataset, which achieve up to 99.74%, 99.69%, 98.80% as classification, precision, and recall accuracy. © 2021 IEEE.COVID-19Deep Neural NetworksFalse PredictionMedical ImagingX-ray Images