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Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network

The Coronavirus disease 2019 (COVID19) pandemic has led to a dramatic loss of human life worldwide and caused a tremendous challenge to public health. Immediate detection and diagnosis of COVID19 have lifesaving importance for both patients and doctors. The availability of COVID19 tests increased si...

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Autores principales: Muralidharan, Neha, Gupta, Shaurya, Prusty, Manas Ranjan, Tripathy, Rajesh Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842414/
https://www.ncbi.nlm.nih.gov/pubmed/35185439
http://dx.doi.org/10.1016/j.asoc.2022.108610
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author Muralidharan, Neha
Gupta, Shaurya
Prusty, Manas Ranjan
Tripathy, Rajesh Kumar
author_facet Muralidharan, Neha
Gupta, Shaurya
Prusty, Manas Ranjan
Tripathy, Rajesh Kumar
author_sort Muralidharan, Neha
collection PubMed
description The Coronavirus disease 2019 (COVID19) pandemic has led to a dramatic loss of human life worldwide and caused a tremendous challenge to public health. Immediate detection and diagnosis of COVID19 have lifesaving importance for both patients and doctors. The availability of COVID19 tests increased significantly in many countries, thereby provisioning a limited availability of laboratory test kits Additionally, the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test for the diagnosis of COVID 19 is costly and time-consuming. X-ray imaging is widely used for the diagnosis of COVID19. The detection of COVID19 based on the manual investigation of X-ray images is a tedious process. Therefore, computer-aided diagnosis (CAD) systems are needed for the automated detection of COVID19 disease. This paper proposes a novel approach for the automated detection of COVID19 using chest X-ray images. The Fixed Boundary-based Two-Dimensional Empirical Wavelet Transform (FB2DEWT) is used to extract modes from the X-ray images. In our study, a single X-ray image is decomposed into seven modes. The evaluated modes are used as input to the multiscale deep Convolutional Neural Network (CNN) to classify X-ray images into no-finding, pneumonia, and COVID19 classes. The proposed deep learning model is evaluated using the X-ray images from two different publicly available databases, where database A consists of 1225 images and database B consists of 9000 images. The results show that the proposed approach has obtained a maximum accuracy of 96% and 100% for the multiclass and binary classification schemes using X-ray images from dataset A with 5-fold cross-validation (CV) strategy. For dataset B, the accuracy values of 97.17% and 96.06% are achieved using multiscale deep CNN for multiclass and binary classification schemes with 5-fold CV. The proposed multiscale deep learning model has demonstrated a higher classification performance than the existing approaches for detecting COVID19 using X-ray images.
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spelling pubmed-88424142022-02-15 Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network Muralidharan, Neha Gupta, Shaurya Prusty, Manas Ranjan Tripathy, Rajesh Kumar Appl Soft Comput Article The Coronavirus disease 2019 (COVID19) pandemic has led to a dramatic loss of human life worldwide and caused a tremendous challenge to public health. Immediate detection and diagnosis of COVID19 have lifesaving importance for both patients and doctors. The availability of COVID19 tests increased significantly in many countries, thereby provisioning a limited availability of laboratory test kits Additionally, the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test for the diagnosis of COVID 19 is costly and time-consuming. X-ray imaging is widely used for the diagnosis of COVID19. The detection of COVID19 based on the manual investigation of X-ray images is a tedious process. Therefore, computer-aided diagnosis (CAD) systems are needed for the automated detection of COVID19 disease. This paper proposes a novel approach for the automated detection of COVID19 using chest X-ray images. The Fixed Boundary-based Two-Dimensional Empirical Wavelet Transform (FB2DEWT) is used to extract modes from the X-ray images. In our study, a single X-ray image is decomposed into seven modes. The evaluated modes are used as input to the multiscale deep Convolutional Neural Network (CNN) to classify X-ray images into no-finding, pneumonia, and COVID19 classes. The proposed deep learning model is evaluated using the X-ray images from two different publicly available databases, where database A consists of 1225 images and database B consists of 9000 images. The results show that the proposed approach has obtained a maximum accuracy of 96% and 100% for the multiclass and binary classification schemes using X-ray images from dataset A with 5-fold cross-validation (CV) strategy. For dataset B, the accuracy values of 97.17% and 96.06% are achieved using multiscale deep CNN for multiclass and binary classification schemes with 5-fold CV. The proposed multiscale deep learning model has demonstrated a higher classification performance than the existing approaches for detecting COVID19 using X-ray images. Elsevier B.V. 2022-04 2022-02-14 /pmc/articles/PMC8842414/ /pubmed/35185439 http://dx.doi.org/10.1016/j.asoc.2022.108610 Text en © 2022 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 Article
Muralidharan, Neha
Gupta, Shaurya
Prusty, Manas Ranjan
Tripathy, Rajesh Kumar
Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network
title Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network
title_full Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network
title_fullStr Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network
title_full_unstemmed Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network
title_short Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network
title_sort detection of covid19 from x-ray images using multiscale deep convolutional neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842414/
https://www.ncbi.nlm.nih.gov/pubmed/35185439
http://dx.doi.org/10.1016/j.asoc.2022.108610
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