Abstract

Coronavirus (COVID-19) is the most prevalent coronavirus infection with respiratory symptoms such as fever, cough, dyspnea, pneumonia, and weariness being typical in the early stages. On the other hand, COVID-19 has a direct impact on the circulatory and respiratory systems as it causes a failure to some human organs or severe respiratory distress in extreme circumstances. Early diagnosis of COVID-19 is extremely important for the medical community to limit its spread. For a large number of suspected cases, manual diagnostic methods based on the analysis of chest images are insufficient. Faced with this situation, artificial intelligence (AI) techniques have shown great potential in automatic diagnostic tasks. This paper aims at proposing a fast and precise medical diagnosis support system (MDSS) that can distinguish COVID-19 precisely in chest-X-ray images. This MDSS uses a concatenation technique that aims to combine pre-trained convolutional neural networks (CNN) depend on the transfer learning (TL) technique to build a highly accurate model. The models enable storage and application of knowledge learned from a pre-trained CNN to a new task, viz., COVID-19 case detection. For this purpose, we employed the concatenation method to aggregate the performances of numerous pre-trained models to confirm the reliability of the proposed method for identifying the patients with COVID-19 disease from X-ray images. The proposed system was trialed on a dataset that included four classes: normal, viral-pneumonia, tuberculosis, and COVID-19 cases. Various general evaluation methods were used to evaluate the effectiveness of the proposed model. The first proposed model achieved an accuracy rate of 99.80% while the second model reached an accuracy of 99.71%.

Highlights

  • Coronaviruses, such as the SARS coronavirus (SARS-CoV-2), MERS coronavirus (MERS-CoV), and SARS-CoV-2, are viruses that frequently cause mild to severe respiratory infections [1]

  • We propose an automated diagnosis system for detecting Coronavirus infected patients based on several transfer learning (TL) models

  • Evaluation methodologies commonly utilized to assess the models of Machine Learning (ML) during the stages of training and testing

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Summary

Introduction

Coronaviruses, such as the SARS coronavirus (SARS-CoV-2), MERS coronavirus (MERS-CoV), and SARS-CoV-2, are viruses that frequently cause mild to severe respiratory infections [1]. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In particular has caused the COVID-19 pandemic, which began in Wuhan, China, before spreading to all countries and territories worldwide, as stated by the World Health Organization (WHO) in 2020 [2]. COVID-19 is the most pervasive coronavirus infection, according to outbreaks of other coronavirus diseases. The early stages of coronavirus are considered by many respiratory symptoms such as fever, cough, dyspnea, pneumonia, and tiredness [3]. Coronavirus affects the circulatory and pulmonary systems, and in severe cases it can result in multiple organ failure or acute respiratory distress [4]

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