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Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

Mohamed Esmail Karar1,2, Marwa Ahmed Shouman3, Claire Chalopin4,*

1 Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering (FEE), Menoufia University, Menouf, 32952, Egypt
2 Department of Computer Engineering and Networks, College of Computing and Information Technology, Shaqra University, Shaqra, 11961, Saudi Arabia
3 Department of Computer Science and Engineering, Faculty of Electronic Engineering (FEE), Menoufia University, Menouf, 32952, Egypt
4 Innovation Center for Computer Assisted Surgery (ICCAS), Universität Leipzig, Leipzig, 04103, Germany

* Corresponding Author: Claire Chalopin. Email: email-leipzig.de

(This article belongs to the Special Issue: Machine Learning Applications in Medical, Finance, Education and Cyber Security)

Computers, Materials & Continua 2022, 70(1), 1683-1697. https://doi.org/10.32604/cmc.2022.018564

Abstract

The novel Coronavirus disease 2019 (COVID-19) pandemic has begun in China and is still affecting thousands of patient lives worldwide daily. Although Chest X-ray and Computed Tomography are the gold standard medical imaging modalities for diagnosing potentially infected COVID-19 cases, applying Ultrasound (US) imaging technique to accomplish this crucial diagnosing task has attracted many physicians recently. In this article, we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images, based on generative adversarial neural networks (GANs). The proposed image classifiers are a semi-supervised GAN and a modified GAN with auxiliary classifier. Each one includes a modified discriminator to identify the class of the US image using semi-supervised learning technique, keeping its main function of defining the “realness” of tested images. Extensive tests have been successfully conducted on public dataset of US images acquired with a convex US probe. This study demonstrated the feasibility of using chest US images with two GAN classifiers as a new radiological tool for clinical check of COVID-19 patients. The results of our proposed GAN models showed that high accuracy values above 91.0% were obtained under different sizes of limited training data, outperforming other deep learning-based methods, such as transfer learning models in the recent studies. Consequently, the clinical implementation of our computer-aided diagnosis of US-COVID-19 is the future work of this study.

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APA Style
Karar, M.E., Shouman, M.A., Chalopin, C. (2022). Adversarial neural network classifiers for COVID-19 diagnosis in ultrasound images. Computers, Materials & Continua, 70(1), 1683-1697. https://doi.org/10.32604/cmc.2022.018564
Vancouver Style
Karar ME, Shouman MA, Chalopin C. Adversarial neural network classifiers for COVID-19 diagnosis in ultrasound images. Comput Mater Contin. 2022;70(1):1683-1697 https://doi.org/10.32604/cmc.2022.018564
IEEE Style
M.E. Karar, M.A. Shouman, and C. Chalopin "Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images," Comput. Mater. Contin., vol. 70, no. 1, pp. 1683-1697. 2022. https://doi.org/10.32604/cmc.2022.018564

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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