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Automated Diagnosis of COVID-19 Using Synthetic Chest X-Ray Images from Generative Adversarial Networks and Blend of Inception-v3 and Vgg-19 Features

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Abstract

The new coronavirus (COVID-19) upsurge continues to grow all around the globe and had killed more than 60 lakh people. This infection can progress into pneumonia which can be identified through chest X-ray (CXR) image investigation. The present diagnostic process uses real-time reverse-transcription polymerase chain reaction (RT-PCR) based detection which is not very sensitive in detecting the virus at the early phase. Therefore, a more powerful and a substitute detection technique is much needed. In this paper, we introduce an automatic COVID-19 infection detection scheme using CXR images. Very few images of people with this infection are openly available due to which the dataset imbalancing and further the overfitting issues may arise. In this work, we used generative adversarial network (GAN) generated synthetic images for the COVID-19, Normal and Pneumonia categories to overcome this issue. We employ various convolutional neural networks (CNNs) such as Resnet50, Vgg-19, Mobilenet-v2, Inception-v3 and Densenet-201 based transfer learning for the purpose of feature extraction from input X-ray images and then these CNNs are combined with machine learning techniques SVM for the COVID-19 infection detection. We exploit the Contrast Limited Adaptive Histogram Equalization(CLAHE) to enhance the contrast-levels of input CXR images. The results demonstrate that the blend of fine-tuned Inception-v3 and Vgg-19 features when fed into the support vector machine (SVM) classifier provides superior results than many other reported techniques for COVID-19 diagnosis. Our technique was able to attain an accuracy of 99.47% in a 3-category classification system, which makes it a promising scheme for diagnosis of COVID-19.

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Data available on request from the authors.

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Acknowledgements

This work was supported by All India Council for Technical Education (AICTE), Govt. of India, through ADF scheme.

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  1. Department of ECE, Tezpur University, Napaam, Tezpur, Assam, 784028, India

    D. Mahanta, D. Hazarika & V. K. Nath

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  1. D. Mahanta
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Correspondence to D. Hazarika.

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This article is part of the topical collection “Enabling Innovative Computational Intelligence Technologies for IOT” guest edited by Omer Rana, Rajiv Misra, Alexander Pfeiffer, Luigi Troiano and Nishtha Kesswani.

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Mahanta, D., Hazarika, D. & Nath, V.K. Automated Diagnosis of COVID-19 Using Synthetic Chest X-Ray Images from Generative Adversarial Networks and Blend of Inception-v3 and Vgg-19 Features. SN COMPUT. SCI. 4, 558 (2023). https://doi.org/10.1007/s42979-023-02002-w

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