Deep Learning Convolutional Neural Network for SARS-CoV-2 Detection Using Chest X-Ray Images

Ali Mohammed Saleh Ahmed, Inteasar Yaseen Khudhair and Salam Abdulkhaleq Noaman

Acta Informatica Pragensia, 2023, vol. 2023, issue 1, 71-86

Abstract: The COVID-19 coronavirus illness is caused by a newly discovered species of coronavirus known as SARS-CoV-2. Since COVID-19 has now expanded across many nations, the World Health Organization (WHO) has designated it a pandemic. Reverse transcription-polymerase chain reaction (RT-PCR) is often used to screen samples of patients showing signs of COVID-19; however, this method is more expensive and takes at least 24 hours to get a positive or negative response. Thus, an immediate and precise method of diagnosis is needed. In this paper, chest X-rays will be utilized through a deep neural network (DNN), based on a convolutional neural network (CNN), to detect COVID-19 infection. Based on their X-rays, those with COVID-19 indications may be categorized as clean, infected with COVID-19 or suffering from pneumonia, according to the suggested CNN network. Sample pieces from every group are used in experiments, and categorization is performed by a CNN. While experimenting, the CNN-derived features were able to generate the maximum training accuracy of 94.82% and validation accuracy of 94.87%. The F1-scores were 97%, 90% and 96%, in clearly categorizing patients afflicted by COVID-19, normal and having pneumonia, respectively. Meanwhile, the recalls are 95%, 91% and 96% for COVID-19, normal and pneumonia, respectively.

Keywords: COVID-19; SARS-CoV-2; CNN; Deep neural network; RT-PCR; Computer model (search for similar items in EconPapers)
Date: 2023
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DOI: 10.18267/j.aip.205

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