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COVID-19 detection on chest X-ray images using Homomorphic Transformation and VGG inspired deep convolutional neural network

COVID-19 had caused the whole world to come to a standstill. The current detection methods are time consuming as well as costly. Using Chest X-rays (CXRs) is a solution to this problem, however, manual examination of CXRs is a cumbersome and difficult process needing specialization in the domain. Mo...

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Autores principales: Shibu George, Gerosh, Raj Mishra, Pratyush, Sinha, Panav, Ranjan Prusty, Manas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9684127/
https://www.ncbi.nlm.nih.gov/pubmed/36447948
http://dx.doi.org/10.1016/j.bbe.2022.11.003
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author Shibu George, Gerosh
Raj Mishra, Pratyush
Sinha, Panav
Ranjan Prusty, Manas
author_facet Shibu George, Gerosh
Raj Mishra, Pratyush
Sinha, Panav
Ranjan Prusty, Manas
author_sort Shibu George, Gerosh
collection PubMed
description COVID-19 had caused the whole world to come to a standstill. The current detection methods are time consuming as well as costly. Using Chest X-rays (CXRs) is a solution to this problem, however, manual examination of CXRs is a cumbersome and difficult process needing specialization in the domain. Most of existing methods used for this application involve the usage of pretrained models such as VGG19, ResNet, DenseNet, Xception, and EfficeintNet which were trained on RGB image datasets. X-rays are fundamentally single channel images, hence using RGB trained model is not appropriate since it increases the operations by involving three channels instead of one. A way of using pretrained model for grayscale images is by replicating the one channel image data to three channel which introduces redundancy and another way is by altering the input layer of pretrained model to take in one channel image data, which comprises the weights in the forward layers that were trained on three channel images which weakens the use of pre-trained weights in a transfer learning approach. A novel approach for identification of COVID-19 using CXRs, Contrast Limited Adaptive Histogram Equalization (CLAHE) along with Homomorphic Transformation Filter which is used to process the pixel data in images and extract features from the CXRs is suggested in this paper. These processed images are then provided as input to a VGG inspired deep Convolutional Neural Network (CNN) model which takes one channel image data as input (grayscale images) to categorize CXRs into three class labels, namely, No-Findings, COVID-19, and Pneumonia. Evaluation of the suggested model is done with the help of two publicly available datasets; one to obtain COVID-19 and No-Finding images and the other to obtain Pneumonia CXRs. The dataset comprises 6750 images in total; 2250 images for each class. Results obtained show that the model has achieved 96.56% for multi-class classification and 98.06% accuracy for binary classification using 5-fold stratified cross validation (CV) method. This result is competitive and up to the mark when compared with the performance shown by existing approaches for COVID-19 classification.
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spelling pubmed-96841272022-11-25 COVID-19 detection on chest X-ray images using Homomorphic Transformation and VGG inspired deep convolutional neural network Shibu George, Gerosh Raj Mishra, Pratyush Sinha, Panav Ranjan Prusty, Manas Biocybern Biomed Eng Original Research Article COVID-19 had caused the whole world to come to a standstill. The current detection methods are time consuming as well as costly. Using Chest X-rays (CXRs) is a solution to this problem, however, manual examination of CXRs is a cumbersome and difficult process needing specialization in the domain. Most of existing methods used for this application involve the usage of pretrained models such as VGG19, ResNet, DenseNet, Xception, and EfficeintNet which were trained on RGB image datasets. X-rays are fundamentally single channel images, hence using RGB trained model is not appropriate since it increases the operations by involving three channels instead of one. A way of using pretrained model for grayscale images is by replicating the one channel image data to three channel which introduces redundancy and another way is by altering the input layer of pretrained model to take in one channel image data, which comprises the weights in the forward layers that were trained on three channel images which weakens the use of pre-trained weights in a transfer learning approach. A novel approach for identification of COVID-19 using CXRs, Contrast Limited Adaptive Histogram Equalization (CLAHE) along with Homomorphic Transformation Filter which is used to process the pixel data in images and extract features from the CXRs is suggested in this paper. These processed images are then provided as input to a VGG inspired deep Convolutional Neural Network (CNN) model which takes one channel image data as input (grayscale images) to categorize CXRs into three class labels, namely, No-Findings, COVID-19, and Pneumonia. Evaluation of the suggested model is done with the help of two publicly available datasets; one to obtain COVID-19 and No-Finding images and the other to obtain Pneumonia CXRs. The dataset comprises 6750 images in total; 2250 images for each class. Results obtained show that the model has achieved 96.56% for multi-class classification and 98.06% accuracy for binary classification using 5-fold stratified cross validation (CV) method. This result is competitive and up to the mark when compared with the performance shown by existing approaches for COVID-19 classification. Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. 2023 2022-11-24 /pmc/articles/PMC9684127/ /pubmed/36447948 http://dx.doi.org/10.1016/j.bbe.2022.11.003 Text en © 2022 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Original Research Article
Shibu George, Gerosh
Raj Mishra, Pratyush
Sinha, Panav
Ranjan Prusty, Manas
COVID-19 detection on chest X-ray images using Homomorphic Transformation and VGG inspired deep convolutional neural network
title COVID-19 detection on chest X-ray images using Homomorphic Transformation and VGG inspired deep convolutional neural network
title_full COVID-19 detection on chest X-ray images using Homomorphic Transformation and VGG inspired deep convolutional neural network
title_fullStr COVID-19 detection on chest X-ray images using Homomorphic Transformation and VGG inspired deep convolutional neural network
title_full_unstemmed COVID-19 detection on chest X-ray images using Homomorphic Transformation and VGG inspired deep convolutional neural network
title_short COVID-19 detection on chest X-ray images using Homomorphic Transformation and VGG inspired deep convolutional neural network
title_sort covid-19 detection on chest x-ray images using homomorphic transformation and vgg inspired deep convolutional neural network
topic Original Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9684127/
https://www.ncbi.nlm.nih.gov/pubmed/36447948
http://dx.doi.org/10.1016/j.bbe.2022.11.003
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