Automatic Diagnosis of Covid-19 Related Pneumonia from CXR and CT-Scan Images


  • N. Kumar Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, India
  • A. Hashmi Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, India
  • M. Gupta Department of Computer Science and Engineering, Moradabad Institute of Technology, India
  • A. Kundu Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, India
Volume: 12 | Issue: 1 | Pages: 7993-7997 | February 2022 |


Covid-19 is a highly infectious disease that spreads extremely fast and is transmitted through indirect or direct contact. The scientists have categorized the Covid-19 cases into five different types: severe, critical, asymptomatic, moderate, and mild. Up to May 2021 more than 133.2 million peoples have been infected and almost 2.9 million people have lost their lives from Covid-19. To diagnose Covid-19, practitioners use RT-PCR tests that suffer from many False Positive (FP) and False Negative (FN) results while they take a long time. One solution to this is the conduction of a greater number of tests simultaneously to improve the True Positive (TP) ratio. However, CT-scan and X-ray images can also be used for early detection of Covid-19 related pneumonia. By the use of modern deep learning techniques, accuracy of more than 95% can be achieved. We used eight CNN (CovNet)-based deep learning models, namely ResNet 152 v2, InceptionResNet v2, Xception, Inception v3, ResNet 50, NASNetLarge, DenseNet 201, and VGG 16 for both X-rays and CT-scans to diagnose pneumonia. The achieved comparative results show that the proposed models are able to differentiate the Covid-19 positive cases.


artificial intelligence, covid-19 detection, convolutional neural networks, deep learning


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How to Cite

N. Kumar, A. Hashmi, M. Gupta, and A. Kundu, “Automatic Diagnosis of Covid-19 Related Pneumonia from CXR and CT-Scan Images”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 1, pp. 7993–7997, Feb. 2022.


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