Automatic Diagnosis of Covid-19 Related Pneumonia from CXR and CT-Scan Images
Received: 13 November 2021 | Revised: 1 December 2021 | Accepted: 4 December 2021 | Online: 15 January 2022
Corresponding author: A. Hashmi
Abstract
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.
Keywords:
artificial intelligence, covid-19 detection, convolutional neural networks, deep learningDownloads
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Copyright (c) 2021 N. Kumar, A. Hashmi, M. Gupta, A. Kundu
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