An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine th...

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Autores principales:	Muhammad Shoaib Farooq, Attique Ur Rehman, Muhammad Idrees, Muhammad Ahsan Raza, Jehad Ali, Mehedi Masud, Jehad F. Al-Amri, Syed Hasnain Raza Kazmi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	convolutional COVID-19 neural network chest X-ray model detection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/cddd60ad1caf4c4d80260d2ec3e38893
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spelling	oai:doaj.org-article:cddd60ad1caf4c4d80260d2ec3e388932021-11-11T15:19:56ZAn Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images10.3390/app1121103012076-3417https://doaj.org/article/cddd60ad1caf4c4d80260d2ec3e388932021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10301https://doaj.org/toc/2076-3417COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.Muhammad Shoaib FarooqAttique Ur RehmanMuhammad IdreesMuhammad Ahsan RazaJehad AliMehedi MasudJehad F. Al-AmriSyed Hasnain Raza KazmiMDPI AGarticleconvolutionalCOVID-19neural networkchest X-raymodeldetectionTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10301, p 10301 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	convolutional COVID-19 neural network chest X-ray model detection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	convolutional COVID-19 neural network chest X-ray model detection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Muhammad Shoaib Farooq Attique Ur Rehman Muhammad Idrees Muhammad Ahsan Raza Jehad Ali Mehedi Masud Jehad F. Al-Amri Syed Hasnain Raza Kazmi An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images
description	COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.
format	article
author	Muhammad Shoaib Farooq Attique Ur Rehman Muhammad Idrees Muhammad Ahsan Raza Jehad Ali Mehedi Masud Jehad F. Al-Amri Syed Hasnain Raza Kazmi
author_facet	Muhammad Shoaib Farooq Attique Ur Rehman Muhammad Idrees Muhammad Ahsan Raza Jehad Ali Mehedi Masud Jehad F. Al-Amri Syed Hasnain Raza Kazmi
author_sort	Muhammad Shoaib Farooq
title	An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images
title_short	An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images
title_full	An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images
title_fullStr	An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images
title_full_unstemmed	An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images
title_sort	effective convolutional neural network model for the early detection of covid-19 using chest x-ray images
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/cddd60ad1caf4c4d80260d2ec3e38893
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An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

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