An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques

The proposed method introduces algorithms for the preprocessing of normal, COVID-19, and pneumonia X-ray lung images which promote the accuracy of classification when compared with raw (unprocessed) X-ray lung images. Preprocessing of an image improves the quality of an image increasing the intersec...

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Autores principales:	Umashankar Subramaniam, M. Monica Subashini, Dhafer Almakhles, Alagar Karthick, S. Manoharan
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Medicine R
Acceso en línea:	https://doaj.org/article/fbbe7a3b3de144c0abd7e1ce1350a3e8
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spelling	oai:doaj.org-article:fbbe7a3b3de144c0abd7e1ce1350a3e82021-11-22T01:11:14ZAn Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques2314-614110.1155/2021/1896762https://doaj.org/article/fbbe7a3b3de144c0abd7e1ce1350a3e82021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1896762https://doaj.org/toc/2314-6141The proposed method introduces algorithms for the preprocessing of normal, COVID-19, and pneumonia X-ray lung images which promote the accuracy of classification when compared with raw (unprocessed) X-ray lung images. Preprocessing of an image improves the quality of an image increasing the intersection over union scores in segmentation of lungs from the X-ray images. The authors have implemented an efficient preprocessing and classification technique for respiratory disease detection. In this proposed method, the histogram of oriented gradients (HOG) algorithm, Haar transform (Haar), and local binary pattern (LBP) algorithm were applied on lung X-ray images to extract the best features and segment the left lung and right lung. The segmentation of lungs from the X-ray can improve the accuracy of results in COVID-19 detection algorithms or any machine/deep learning techniques. The segmented lungs are validated over intersection over union scores to compare the algorithms. The preprocessed X-ray image results in better accuracy in classification for all three classes (normal/COVID-19/pneumonia) than unprocessed raw images. VGGNet, AlexNet, Resnet, and the proposed deep neural network were implemented for the classification of respiratory diseases. Among these architectures, the proposed deep neural network outperformed the other models with better classification accuracy.Umashankar SubramaniamM. Monica SubashiniDhafer AlmakhlesAlagar KarthickS. ManoharanHindawi LimitedarticleMedicineRENBioMed Research International, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R
spellingShingle	Medicine R Umashankar Subramaniam M. Monica Subashini Dhafer Almakhles Alagar Karthick S. Manoharan An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques
description	The proposed method introduces algorithms for the preprocessing of normal, COVID-19, and pneumonia X-ray lung images which promote the accuracy of classification when compared with raw (unprocessed) X-ray lung images. Preprocessing of an image improves the quality of an image increasing the intersection over union scores in segmentation of lungs from the X-ray images. The authors have implemented an efficient preprocessing and classification technique for respiratory disease detection. In this proposed method, the histogram of oriented gradients (HOG) algorithm, Haar transform (Haar), and local binary pattern (LBP) algorithm were applied on lung X-ray images to extract the best features and segment the left lung and right lung. The segmentation of lungs from the X-ray can improve the accuracy of results in COVID-19 detection algorithms or any machine/deep learning techniques. The segmented lungs are validated over intersection over union scores to compare the algorithms. The preprocessed X-ray image results in better accuracy in classification for all three classes (normal/COVID-19/pneumonia) than unprocessed raw images. VGGNet, AlexNet, Resnet, and the proposed deep neural network were implemented for the classification of respiratory diseases. Among these architectures, the proposed deep neural network outperformed the other models with better classification accuracy.
format	article
author	Umashankar Subramaniam M. Monica Subashini Dhafer Almakhles Alagar Karthick S. Manoharan
author_facet	Umashankar Subramaniam M. Monica Subashini Dhafer Almakhles Alagar Karthick S. Manoharan
author_sort	Umashankar Subramaniam
title	An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques
title_short	An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques
title_full	An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques
title_fullStr	An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques
title_full_unstemmed	An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques
title_sort	expert system for covid-19 infection tracking in lungs using image processing and deep learning techniques
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/fbbe7a3b3de144c0abd7e1ce1350a3e8
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An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques

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