Deep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images

Early diagnosis of the harmful severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), along with clinical expertise, allows governments to break the transition chain and flatten the epidemic curve. Although reverse transcription-polymerase chain reaction (RT-PCR) offers quick results, chest X...

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Autores principales:	Sharmila V J, Jemi Florinabel D
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/a61e6ce1e17c45d2928df15100ad403b
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spelling	oai:doaj.org-article:a61e6ce1e17c45d2928df15100ad403b2021-11-22T01:10:03ZDeep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images1748-671810.1155/2021/9269173https://doaj.org/article/a61e6ce1e17c45d2928df15100ad403b2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9269173https://doaj.org/toc/1748-6718Early diagnosis of the harmful severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), along with clinical expertise, allows governments to break the transition chain and flatten the epidemic curve. Although reverse transcription-polymerase chain reaction (RT-PCR) offers quick results, chest X-ray (CXR) imaging is a more reliable method for disease classification and assessment. The rapid spread of the coronavirus disease 2019 (COVID-19) has triggered extensive research towards developing a COVID-19 detection toolkit. Recent studies have confirmed that the deep learning-based approach, such as convolutional neural networks (CNNs), provides an optimized solution for COVID-19 classification; however, they require substantial training data for learning features. Gathering this training data in a short period has been challenging during the pandemic. Therefore, this study proposes a new model of CNN and deep convolutional generative adversarial networks (DCGANs) that classify CXR images into normal, pneumonia, and COVID-19. The proposed model contains eight convolutional layers, four max-pooling layers, and two fully connected layers, which provide better results than the existing pretrained methods (AlexNet and GoogLeNet). DCGAN performs two tasks: (1) generating synthetic/fake images to overcome the challenges of an imbalanced dataset and (2) extracting deep features of all images in the dataset. In addition, it enlarges the dataset and represents the characteristics of diversity to provide a good generalization effect. In the experimental analysis, we used four distinct publicly accessible datasets of chest X-ray images (COVID-19 X-ray, COVID Chest X-ray, COVID-19 Radiography, and CoronaHack-Chest X-Ray) to train and test the proposed CNN and the existing pretrained methods. Thereafter, the proposed CNN method was trained with the four datasets based on the DCGAN synthetic images, resulting in higher accuracy (94.8%, 96.6%, 98.5%, and 98.6%) than the existing pretrained models. The overall results suggest that the proposed DCGAN-CNN approach is a promising solution for efficient COVID-19 diagnosis.Sharmila V JJemi Florinabel DHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7ENComputational and Mathematical Methods in Medicine, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Sharmila V J Jemi Florinabel D Deep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images
description	Early diagnosis of the harmful severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), along with clinical expertise, allows governments to break the transition chain and flatten the epidemic curve. Although reverse transcription-polymerase chain reaction (RT-PCR) offers quick results, chest X-ray (CXR) imaging is a more reliable method for disease classification and assessment. The rapid spread of the coronavirus disease 2019 (COVID-19) has triggered extensive research towards developing a COVID-19 detection toolkit. Recent studies have confirmed that the deep learning-based approach, such as convolutional neural networks (CNNs), provides an optimized solution for COVID-19 classification; however, they require substantial training data for learning features. Gathering this training data in a short period has been challenging during the pandemic. Therefore, this study proposes a new model of CNN and deep convolutional generative adversarial networks (DCGANs) that classify CXR images into normal, pneumonia, and COVID-19. The proposed model contains eight convolutional layers, four max-pooling layers, and two fully connected layers, which provide better results than the existing pretrained methods (AlexNet and GoogLeNet). DCGAN performs two tasks: (1) generating synthetic/fake images to overcome the challenges of an imbalanced dataset and (2) extracting deep features of all images in the dataset. In addition, it enlarges the dataset and represents the characteristics of diversity to provide a good generalization effect. In the experimental analysis, we used four distinct publicly accessible datasets of chest X-ray images (COVID-19 X-ray, COVID Chest X-ray, COVID-19 Radiography, and CoronaHack-Chest X-Ray) to train and test the proposed CNN and the existing pretrained methods. Thereafter, the proposed CNN method was trained with the four datasets based on the DCGAN synthetic images, resulting in higher accuracy (94.8%, 96.6%, 98.5%, and 98.6%) than the existing pretrained models. The overall results suggest that the proposed DCGAN-CNN approach is a promising solution for efficient COVID-19 diagnosis.
format	article
author	Sharmila V J Jemi Florinabel D
author_facet	Sharmila V J Jemi Florinabel D
author_sort	Sharmila V J
title	Deep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images
title_short	Deep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images
title_full	Deep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images
title_fullStr	Deep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images
title_full_unstemmed	Deep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images
title_sort	deep learning algorithm for covid-19 classification using chest x-ray images
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/a61e6ce1e17c45d2928df15100ad403b
work_keys_str_mv	AT sharmilavj deeplearningalgorithmforcovid19classificationusingchestxrayimages AT jemiflorinabeld deeplearningalgorithmforcovid19classificationusingchestxrayimages
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Deep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images

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