COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques

It became apparent that mankind has to learn to live with and adapt to COVID-19, especially because the developed vaccines thus far do not prevent the infection but rather just reduce the severity of the symptoms. The manual classification and diagnosis of COVID-19 pneumonia requires specialized per...

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Autores principales:	Abul Bashar, Ghazanfar Latif, Ghassen Ben Brahim, Nazeeruddin Mohammad, Jaafar Alghazo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	COVID-19 detection chest X-ray convolutional neural networks lung opacity detection viral pneumonia detection Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/80ce75eea82b4908a177d284087e6a52
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spelling	oai:doaj.org-article:80ce75eea82b4908a177d284087e6a522021-11-25T17:20:22ZCOVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques10.3390/diagnostics111119722075-4418https://doaj.org/article/80ce75eea82b4908a177d284087e6a522021-10-01T00:00:00Zhttps://www.mdpi.com/2075-4418/11/11/1972https://doaj.org/toc/2075-4418It became apparent that mankind has to learn to live with and adapt to COVID-19, especially because the developed vaccines thus far do not prevent the infection but rather just reduce the severity of the symptoms. The manual classification and diagnosis of COVID-19 pneumonia requires specialized personnel and is time consuming and very costly. On the other hand, automatic diagnosis would allow for real-time diagnosis without human intervention resulting in reduced costs. Therefore, the objective of this research is to propose a novel optimized Deep Learning (DL) approach for the automatic classification and diagnosis of COVID-19 pneumonia using X-ray images. For this purpose, a publicly available dataset of chest X-rays on Kaggle was used in this study. The dataset was developed over three stages in a quest to have a unified COVID-19 entities dataset available for researchers. The dataset consists of 21,165 anterior-to-posterior and posterior-to-anterior chest X-ray images classified as: Normal (48%), COVID-19 (17%), Lung Opacity (28%) and Viral Pneumonia (6%). Data Augmentation was also applied to increase the dataset size to enhance the reliability of results by preventing overfitting. An optimized DL approach is implemented in which chest X-ray images go through a three-stage process. Image Enhancement is performed in the first stage, followed by Data Augmentation stage and in the final stage the results are fed to the Transfer Learning algorithms (AlexNet, GoogleNet, VGG16, VGG19, and DenseNet) where the images are classified and diagnosed. Extensive experiments were performed under various scenarios, which led to achieving the highest classification accuracy of 95.63% through the application of VGG16 transfer learning algorithm on the augmented enhanced dataset with freeze weights. This accuracy was found to be better as compared to the results reported by other methods in the recent literature. Thus, the proposed approach proved superior in performance as compared with that of other similar approaches in the extant literature, and it made a valuable contribution to the body of knowledge. Although the results achieved so far are promising, further work is planned to correlate the results of the proposed approach with clinical observations to further enhance the efficiency and accuracy of COVID-19 diagnosis.Abul BasharGhazanfar LatifGhassen Ben BrahimNazeeruddin MohammadJaafar AlghazoMDPI AGarticleCOVID-19 detectionchest X-rayconvolutional neural networkslung opacity detectionviral pneumonia detectionMedicine (General)R5-920ENDiagnostics, Vol 11, Iss 1972, p 1972 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 detection chest X-ray convolutional neural networks lung opacity detection viral pneumonia detection Medicine (General) R5-920
spellingShingle	COVID-19 detection chest X-ray convolutional neural networks lung opacity detection viral pneumonia detection Medicine (General) R5-920 Abul Bashar Ghazanfar Latif Ghassen Ben Brahim Nazeeruddin Mohammad Jaafar Alghazo COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques
description	It became apparent that mankind has to learn to live with and adapt to COVID-19, especially because the developed vaccines thus far do not prevent the infection but rather just reduce the severity of the symptoms. The manual classification and diagnosis of COVID-19 pneumonia requires specialized personnel and is time consuming and very costly. On the other hand, automatic diagnosis would allow for real-time diagnosis without human intervention resulting in reduced costs. Therefore, the objective of this research is to propose a novel optimized Deep Learning (DL) approach for the automatic classification and diagnosis of COVID-19 pneumonia using X-ray images. For this purpose, a publicly available dataset of chest X-rays on Kaggle was used in this study. The dataset was developed over three stages in a quest to have a unified COVID-19 entities dataset available for researchers. The dataset consists of 21,165 anterior-to-posterior and posterior-to-anterior chest X-ray images classified as: Normal (48%), COVID-19 (17%), Lung Opacity (28%) and Viral Pneumonia (6%). Data Augmentation was also applied to increase the dataset size to enhance the reliability of results by preventing overfitting. An optimized DL approach is implemented in which chest X-ray images go through a three-stage process. Image Enhancement is performed in the first stage, followed by Data Augmentation stage and in the final stage the results are fed to the Transfer Learning algorithms (AlexNet, GoogleNet, VGG16, VGG19, and DenseNet) where the images are classified and diagnosed. Extensive experiments were performed under various scenarios, which led to achieving the highest classification accuracy of 95.63% through the application of VGG16 transfer learning algorithm on the augmented enhanced dataset with freeze weights. This accuracy was found to be better as compared to the results reported by other methods in the recent literature. Thus, the proposed approach proved superior in performance as compared with that of other similar approaches in the extant literature, and it made a valuable contribution to the body of knowledge. Although the results achieved so far are promising, further work is planned to correlate the results of the proposed approach with clinical observations to further enhance the efficiency and accuracy of COVID-19 diagnosis.
format	article
author	Abul Bashar Ghazanfar Latif Ghassen Ben Brahim Nazeeruddin Mohammad Jaafar Alghazo
author_facet	Abul Bashar Ghazanfar Latif Ghassen Ben Brahim Nazeeruddin Mohammad Jaafar Alghazo
author_sort	Abul Bashar
title	COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques
title_short	COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques
title_full	COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques
title_fullStr	COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques
title_full_unstemmed	COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques
title_sort	covid-19 pneumonia detection using optimized deep learning techniques
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/80ce75eea82b4908a177d284087e6a52
work_keys_str_mv	AT abulbashar covid19pneumoniadetectionusingoptimizeddeeplearningtechniques AT ghazanfarlatif covid19pneumoniadetectionusingoptimizeddeeplearningtechniques AT ghassenbenbrahim covid19pneumoniadetectionusingoptimizeddeeplearningtechniques AT nazeeruddinmohammad covid19pneumoniadetectionusingoptimizeddeeplearningtechniques AT jaafaralghazo covid19pneumoniadetectionusingoptimizeddeeplearningtechniques
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COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques

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