COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images

COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliab...

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Autores principales:	Shamima Akter, F. M. Javed Mehedi Shamrat, Sovon Chakraborty, Asif Karim, Sami Azam
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	COVID-19 chest X-ray image CNN Mobilenetv2 modified MobileNetV2 performance evaluation Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/ce14ad7849a74985910005a3d5b88a93
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spelling	oai:doaj.org-article:ce14ad7849a74985910005a3d5b88a932021-11-25T16:47:41ZCOVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images10.3390/biology101111742079-7737https://doaj.org/article/ce14ad7849a74985910005a3d5b88a932021-11-01T00:00:00Zhttps://www.mdpi.com/2079-7737/10/11/1174https://doaj.org/toc/2079-7737COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliable, and accurate screening of COVID-19 infection would be beneficial for rapid detection and reducing medical or healthcare professional exposure to the virus. In the past, Convolutional Neural Networks (CNNs) proved to be quite successful in the classification of medical images. In this study, an automatic deep learning classification method for detecting COVID-19 from chest X-ray images is suggested using a CNN. A dataset consisting of 3616 COVID-19 chest X-ray images and 10,192 healthy chest X-ray images was used. The original data were then augmented to increase the data sample to 26,000 COVID-19 and 26,000 healthy X-ray images. The dataset was enhanced using histogram equalization, spectrum, grays, cyan and normalized with NCLAHE before being applied to CNN models. Initially using the dataset, the symptoms of COVID-19 were detected by employing eleven existing CNN models; VGG16, VGG19, MobileNetV2, InceptionV3, NFNet, ResNet50, ResNet101, DenseNet, EfficientNetB7, AlexNet, and GoogLeNet. From the models, MobileNetV2 was selected for further modification to obtain a higher accuracy of COVID-19 detection. Performance evaluation of the models was demonstrated using a confusion matrix. It was observed that the modified MobileNetV2 model proposed in the study gave the highest accuracy of 98% in classifying COVID-19 and healthy chest X-rays among all the implemented CNN models. The second-best performance was achieved from the pre-trained MobileNetV2 with an accuracy of 97%, followed by VGG19 and ResNet101 with 95% accuracy for both the models. The study compares the compilation time of the models. The proposed model required the least compilation time with 2 h, 50 min and 21 s. Finally, the Wilcoxon signed-rank test was performed to test the statistical significance. The results suggest that the proposed method can efficiently identify the symptoms of infection from chest X-ray images better than existing methods.Shamima AkterF. M. Javed Mehedi ShamratSovon ChakrabortyAsif KarimSami AzamMDPI AGarticleCOVID-19chest X-ray imageCNNMobilenetv2modified MobileNetV2performance evaluationBiology (General)QH301-705.5ENBiology, Vol 10, Iss 1174, p 1174 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 chest X-ray image CNN Mobilenetv2 modified MobileNetV2 performance evaluation Biology (General) QH301-705.5
spellingShingle	COVID-19 chest X-ray image CNN Mobilenetv2 modified MobileNetV2 performance evaluation Biology (General) QH301-705.5 Shamima Akter F. M. Javed Mehedi Shamrat Sovon Chakraborty Asif Karim Sami Azam COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images
description	COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliable, and accurate screening of COVID-19 infection would be beneficial for rapid detection and reducing medical or healthcare professional exposure to the virus. In the past, Convolutional Neural Networks (CNNs) proved to be quite successful in the classification of medical images. In this study, an automatic deep learning classification method for detecting COVID-19 from chest X-ray images is suggested using a CNN. A dataset consisting of 3616 COVID-19 chest X-ray images and 10,192 healthy chest X-ray images was used. The original data were then augmented to increase the data sample to 26,000 COVID-19 and 26,000 healthy X-ray images. The dataset was enhanced using histogram equalization, spectrum, grays, cyan and normalized with NCLAHE before being applied to CNN models. Initially using the dataset, the symptoms of COVID-19 were detected by employing eleven existing CNN models; VGG16, VGG19, MobileNetV2, InceptionV3, NFNet, ResNet50, ResNet101, DenseNet, EfficientNetB7, AlexNet, and GoogLeNet. From the models, MobileNetV2 was selected for further modification to obtain a higher accuracy of COVID-19 detection. Performance evaluation of the models was demonstrated using a confusion matrix. It was observed that the modified MobileNetV2 model proposed in the study gave the highest accuracy of 98% in classifying COVID-19 and healthy chest X-rays among all the implemented CNN models. The second-best performance was achieved from the pre-trained MobileNetV2 with an accuracy of 97%, followed by VGG19 and ResNet101 with 95% accuracy for both the models. The study compares the compilation time of the models. The proposed model required the least compilation time with 2 h, 50 min and 21 s. Finally, the Wilcoxon signed-rank test was performed to test the statistical significance. The results suggest that the proposed method can efficiently identify the symptoms of infection from chest X-ray images better than existing methods.
format	article
author	Shamima Akter F. M. Javed Mehedi Shamrat Sovon Chakraborty Asif Karim Sami Azam
author_facet	Shamima Akter F. M. Javed Mehedi Shamrat Sovon Chakraborty Asif Karim Sami Azam
author_sort	Shamima Akter
title	COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images
title_short	COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images
title_full	COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images
title_fullStr	COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images
title_full_unstemmed	COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images
title_sort	covid-19 detection using deep learning algorithm on chest x-ray images
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ce14ad7849a74985910005a3d5b88a93
work_keys_str_mv	AT shamimaakter covid19detectionusingdeeplearningalgorithmonchestxrayimages AT fmjavedmehedishamrat covid19detectionusingdeeplearningalgorithmonchestxrayimages AT sovonchakraborty covid19detectionusingdeeplearningalgorithmonchestxrayimages AT asifkarim covid19detectionusingdeeplearningalgorithmonchestxrayimages AT samiazam covid19detectionusingdeeplearningalgorithmonchestxrayimages
_version_	1718412982631792640

COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images

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