A Novel Approach to detect COVID-19 from chest X-ray images using CNN

Kurmala Marthanda Pradeep; Raghusai  Vemuri; Veeranjaneyulu N

doi:10.34256/ijcci2315

Kurmala Marthanda Pradeep Department of IT, Vignan’s Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, Guntur, Andhra Pradesh, India.
Raghusai Vemuri Department of IT, Vignan’s Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, Guntur, Andhra Pradesh, India.
Veeranjaneyulu N Department of IT, Vignan’s Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, Guntur, Andhra Pradesh, India.

Keywords: COVID-19, VGG-16, Chest X-rays, Pneumonia, ResNet 50

Abstract

In light of the present COVID-19 pandemic, it is important to consider the worth of human life, prosperity, and quality of life while also realizing that it is difficult to restrict case spread and mortality. One of the most difficult challenges for practitioners is identifying individuals who are COVID19-infected and isolating patients to stop COVID transmission. Therefore, identifying the covid19 infection is important. For the detection of COVID-19, a 4-6-hour reverse transcriptase chain reaction is used. Chest X-rays provide us with a different method for detecting Coronavirus early in the disease phase. We detected properties from chest X-ray scans and divided them into three categories with VGG16 as well as ResNet50 deep learning algorithms: COVID-19, normal, and viral pneumonia. To test the model's accuracy in specialized cases, we injected them with 15153 scans. The average COVID-19 case detection accuracy for the ResNet50 model is 91.39%, compared to 89.34% for the VGG16 model. However, a larger dataset is required when using deep learning to identify COVID-19. It accurately detects situations, which is the desired outcome.

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