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Classification of X-ray images into COVID-19, pneumonia, and TB using cGAN and fine-tuned deep transfer learning models

PURPOSE: The rapid increase in the spread of the coronavirus disease of 2019 (COVID-19) has led to a need for reliable, effective, and readily available testing on a large scale. While diagnostic testing has been a support to public health, newer technology can be used to provide low-cost and conven...

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Autores principales: Mehta, Tirth, Mehendale, Ninad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8494631/
http://dx.doi.org/10.1007/s42600-021-00174-z
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author Mehta, Tirth
Mehendale, Ninad
author_facet Mehta, Tirth
Mehendale, Ninad
author_sort Mehta, Tirth
collection PubMed
description PURPOSE: The rapid increase in the spread of the coronavirus disease of 2019 (COVID-19) has led to a need for reliable, effective, and readily available testing on a large scale. While diagnostic testing has been a support to public health, newer technology can be used to provide low-cost and convenient test options for patients. X-ray scanning can be performed to resolve this issue and produce quicker and more precise results. Currently, a radiologist is required to examine these X-ray images. However, deep convolutional neural networks can also be used to perform X-ray examinations and employed for the detection of COVID-19. We propose a Conditional Generative Adversarial Network (cGAN) with a fine-tuned deep transfer learning model to classify chest X-rays into six categories: COVID-Mild, COVID-Medium, COVID-Severe, Normal, Pneumonia, and Tuberculosis. METHODS: A total of 1229 images were taken to form a dataset containing six classes corresponding to the six categories. A cGAN was used to increase the number of images. Generative Adversarial Networks (GAN) are used to train a model for generating new images. cGAN is an extension of GAN consisting of a generator and discriminator network that are trained simultaneously to optimize the model. The generated images were then trained using deep transfer learning models such as ResNet50, Xception, and DenseNet-169 to achieve the classification into six classes. RESULTS: The proposed model helped achieve a training and validation accuracy of up to 98.20 % and 94.21 % respectively. The model was able to achieve a test accuracy of 93.67 %. The use of cGAN not only helped to increase the size of the training dataset but it also helped to reduce the problem of over-fitting. CONCLUSION: The proposed approach will help to diagnose COVID-19 quickly at an early stage. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42600-021-00174-z.
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spelling pubmed-84946312021-10-08 Classification of X-ray images into COVID-19, pneumonia, and TB using cGAN and fine-tuned deep transfer learning models Mehta, Tirth Mehendale, Ninad Res. Biomed. Eng. Original Article PURPOSE: The rapid increase in the spread of the coronavirus disease of 2019 (COVID-19) has led to a need for reliable, effective, and readily available testing on a large scale. While diagnostic testing has been a support to public health, newer technology can be used to provide low-cost and convenient test options for patients. X-ray scanning can be performed to resolve this issue and produce quicker and more precise results. Currently, a radiologist is required to examine these X-ray images. However, deep convolutional neural networks can also be used to perform X-ray examinations and employed for the detection of COVID-19. We propose a Conditional Generative Adversarial Network (cGAN) with a fine-tuned deep transfer learning model to classify chest X-rays into six categories: COVID-Mild, COVID-Medium, COVID-Severe, Normal, Pneumonia, and Tuberculosis. METHODS: A total of 1229 images were taken to form a dataset containing six classes corresponding to the six categories. A cGAN was used to increase the number of images. Generative Adversarial Networks (GAN) are used to train a model for generating new images. cGAN is an extension of GAN consisting of a generator and discriminator network that are trained simultaneously to optimize the model. The generated images were then trained using deep transfer learning models such as ResNet50, Xception, and DenseNet-169 to achieve the classification into six classes. RESULTS: The proposed model helped achieve a training and validation accuracy of up to 98.20 % and 94.21 % respectively. The model was able to achieve a test accuracy of 93.67 %. The use of cGAN not only helped to increase the size of the training dataset but it also helped to reduce the problem of over-fitting. CONCLUSION: The proposed approach will help to diagnose COVID-19 quickly at an early stage. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42600-021-00174-z. Springer International Publishing 2021-10-07 2021 /pmc/articles/PMC8494631/ http://dx.doi.org/10.1007/s42600-021-00174-z Text en © Sociedade Brasileira de Engenharia Biomedica 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Article
Mehta, Tirth
Mehendale, Ninad
Classification of X-ray images into COVID-19, pneumonia, and TB using cGAN and fine-tuned deep transfer learning models
title Classification of X-ray images into COVID-19, pneumonia, and TB using cGAN and fine-tuned deep transfer learning models
title_full Classification of X-ray images into COVID-19, pneumonia, and TB using cGAN and fine-tuned deep transfer learning models
title_fullStr Classification of X-ray images into COVID-19, pneumonia, and TB using cGAN and fine-tuned deep transfer learning models
title_full_unstemmed Classification of X-ray images into COVID-19, pneumonia, and TB using cGAN and fine-tuned deep transfer learning models
title_short Classification of X-ray images into COVID-19, pneumonia, and TB using cGAN and fine-tuned deep transfer learning models
title_sort classification of x-ray images into covid-19, pneumonia, and tb using cgan and fine-tuned deep transfer learning models
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8494631/
http://dx.doi.org/10.1007/s42600-021-00174-z
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