Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images

Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets, networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively. Standard data augmentatio...

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Autores principales:	Saman Motamed, Patrik Rogalla, Farzad Khalvati
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Data augmentation Semi-supervised learning Generative adversarial networks Disease detection Medical Imaging Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/5840f55202eb42e8ad8690506a751581
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id	oai:doaj.org-article:5840f55202eb42e8ad8690506a751581
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spelling	oai:doaj.org-article:5840f55202eb42e8ad8690506a7515812021-11-26T04:35:08ZData augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images2352-914810.1016/j.imu.2021.100779https://doaj.org/article/5840f55202eb42e8ad8690506a7515812021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352914821002501https://doaj.org/toc/2352-9148Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets, networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively. Standard data augmentation methods, however, produce limited plausible alternative data. Generative Adversarial Networks (GANs) have been utilized to generate new data and improve the performance of CNNs. Nevertheless, data augmentation techniques for training GANs are underexplored compared to CNNs. In this work, we propose a new GAN architecture for augmentation of chest X-rays for semi-supervised detection of pneumonia and COVID-19 using generative models. We show that the proposed GAN can be used to effectively augment data and improve classification accuracy of disease in chest X-rays for pneumonia and COVID-19. We compare our augmentation GAN model with Deep Convolutional GAN and traditional augmentation methods (rotate, zoom, etc.) on two different X-ray datasets and show our GAN-based augmentation method surpasses other augmentation methods for training a GAN in detecting anomalies in X-ray images.Saman MotamedPatrik RogallaFarzad KhalvatiElsevierarticleData augmentationSemi-supervised learningGenerative adversarial networksDisease detectionMedical ImagingComputer applications to medicine. Medical informaticsR858-859.7ENInformatics in Medicine Unlocked, Vol 27, Iss , Pp 100779- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Data augmentation Semi-supervised learning Generative adversarial networks Disease detection Medical Imaging Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Data augmentation Semi-supervised learning Generative adversarial networks Disease detection Medical Imaging Computer applications to medicine. Medical informatics R858-859.7 Saman Motamed Patrik Rogalla Farzad Khalvati Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images
description	Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets, networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively. Standard data augmentation methods, however, produce limited plausible alternative data. Generative Adversarial Networks (GANs) have been utilized to generate new data and improve the performance of CNNs. Nevertheless, data augmentation techniques for training GANs are underexplored compared to CNNs. In this work, we propose a new GAN architecture for augmentation of chest X-rays for semi-supervised detection of pneumonia and COVID-19 using generative models. We show that the proposed GAN can be used to effectively augment data and improve classification accuracy of disease in chest X-rays for pneumonia and COVID-19. We compare our augmentation GAN model with Deep Convolutional GAN and traditional augmentation methods (rotate, zoom, etc.) on two different X-ray datasets and show our GAN-based augmentation method surpasses other augmentation methods for training a GAN in detecting anomalies in X-ray images.
format	article
author	Saman Motamed Patrik Rogalla Farzad Khalvati
author_facet	Saman Motamed Patrik Rogalla Farzad Khalvati
author_sort	Saman Motamed
title	Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images
title_short	Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images
title_full	Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images
title_fullStr	Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images
title_full_unstemmed	Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images
title_sort	data augmentation using generative adversarial networks (gans) for gan-based detection of pneumonia and covid-19 in chest x-ray images
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/5840f55202eb42e8ad8690506a751581
work_keys_str_mv	AT samanmotamed dataaugmentationusinggenerativeadversarialnetworksgansforganbaseddetectionofpneumoniaandcovid19inchestxrayimages AT patrikrogalla dataaugmentationusinggenerativeadversarialnetworksgansforganbaseddetectionofpneumoniaandcovid19inchestxrayimages AT farzadkhalvati dataaugmentationusinggenerativeadversarialnetworksgansforganbaseddetectionofpneumoniaandcovid19inchestxrayimages
_version_	1718409842535694336

Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images

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