Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images

Sheetal Rajpal, Navin Lakhyani, Ayush Kumar Singh, Rishav Kohli and Naveen Kumar

Chaos, Solitons & Fractals, 2021, vol. 145, issue C

Abstract: Coronaviruses are a family of viruses that majorly cause respiratory disorders in humans. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new strain of coronavirus that causes the coronavirus disease 2019 (COVID-19). WHO has identified COVID-19 as a pandemic as it has spread across the globe due to its highly contagious nature. For early diagnosis of COVID-19, the reverse transcription-polymerase chain reaction (RT-PCR) test is commonly done. However, it suffers from a high false-negative rate of up to 67% if the test is done during the first five days of exposure. As an alternative, research on the efficacy of deep learning techniques employed in the identification of COVID-19 disease using chest X-ray images is intensely pursued.

Keywords: COVID-19; Machine Learning; Classification; Chest X-Rays; Deep Learning; Grad-CAM (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:145:y:2021:i:c:s0960077921001028

DOI: 10.1016/j.chaos.2021.110749

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