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. 2021 Jul;39(10):3615-3626.
doi: 10.1080/07391102.2020.1767212. Epub 2020 May 22.

Using X-ray images and deep learning for automated detection of coronavirus disease

Khalid El Asnaoui  1 , Youness Chawki  2
Affiliations

Using X-ray images and deep learning for automated detection of coronavirus disease

Khalid El Asnaoui et al. J Biomol Struct Dyn. 2021 Jul.

Abstract

Coronavirus is still the leading cause of death worldwide. There are a set number of COVID-19 test units accessible in emergency clinics because of the expanding cases daily. Therefore, it is important to implement an automatic detection and classification system as a speedy elective finding choice to forestall COVID-19 spreading among individuals. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as Coronavirus. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, and MobileNet_V2) to deal with detection and classification of coronavirus pneumonia. The experiments were conducted using chest X-ray & CT dataset of 6087 images (2780 images of bacterial pneumonia, 1493 of coronavirus, 231 of Covid19, and 1583 normal) and confusion matrices are used to evaluate model performances. Results found out that the use of inception_Resnet_V2 and Densnet201 provide better results compared to other models used in this work (92.18% accuracy for Inception-ResNetV2 and 88.09% accuracy for Densnet201).Communicated by Ramaswamy H. Sarma.

Keywords: CT and X-ray images; Computer-aided diagnosis; Covid-19; coronavirus automatic detection; deep learning; pneumonia.

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Figures

Figure 1.
Figure 1.
Block diagram of the proposed methodology.
Figure 2.
Figure 2.
Examples of Chest X-rays in patients with pneumonia.
Figure 3.
Figure 3.
Confusion matrix of DensNet201.
Figure 4.
Figure 4.
Confusion matrix of Inception_ResNet_V2.
Figure 5.
Figure 5.
Confusion matrix of Inception_V3.
Figure 6.
Figure 6.
Confusion matrix of Mobilenet_V2.
Figure 7.
Figure 7.
Confusion matrix of Resnet50.
Figure 8.
Figure 8.
Confusion matrix of VGG16.
Figure 9.
Figure 9.
Confusion matrix of VGG19.
See this image and copyright information in PMC

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