New optimized deep learning application for COVID-19 detection in chest x-ray images

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dc.contributor.authorKarim, Ahmad M.
dc.contributor.authorKaya, Hilal
dc.contributor.authorAlcan, Veysel
dc.contributor.authorSen, Baha
dc.contributor.authorHadimlioglu, Ismail Alihan
dc.creator.orcidhttps://orcid.org/0000-0003-1588-1245en_US
dc.creator.orcidhttps://orcid.org/0000-0003-1588-1245
dc.date.accessioned2022-09-20T15:32:44Z
dc.date.available2022-09-20T15:32:44Z
dc.date.issued2022-05-14
dc.description.abstractDue to false negative results of the real-time Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) test, the complemental practices such as computed tomography (CT) and X-ray in combination with RT-PCR are discussed to achieve a more accurate diagnosis of COVID-19 in clinical practice. Since radiology includes visual understanding as well as decision making under limited conditions such as uncertainty, urgency, patient burden, and hospital facilities, mistakes are inevitable. Therefore, there is an immediate requirement to carry out further investigation and develop new accurate detection and identification methods to provide automatically quantitative evaluation of COVID-19. In this paper, we propose a new computer-aided diagnosis application for COVID-19 detection using deep learning techniques. A new technique, which receives symmetric X-ray data as the input, is presented in this study by combining Convolutional Neural Networks (CNN) with Ant Lion Optimization Algorithm (ALO) and Multiclass Naïve Bayes Classifier (NB). Moreover, several other classifiers such as Softmax, Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and Decision Tree (DT) are combined with CNN. The promising results of these classifiers are evaluated and presented for accuracy, precision, and F1-score metrics. NB classifier with Ant Lion Optimization Algorithm and CNN produced the best results with 98.31% accuracy, 100% precision and 98.25% F1-score and with the lowest execution time.en_US
dc.description.abstractDue to false negative results of the real-time Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) test, the complemental practices such as computed tomography (CT) and X-ray in combination with RT-PCR are discussed to achieve a more accurate diagnosis of COVID-19 in clinical practice. Since radiology includes visual understanding as well as decision making under limited conditions such as uncertainty, urgency, patient burden, and hospital facilities, mistakes are inevitable. Therefore, there is an immediate requirement to carry out further investigation and develop new accurate detection and identification methods to provide automatically quantitative evaluation of COVID-19. In this paper, we propose a new computer-aided diagnosis application for COVID-19 detection using deep learning techniques. A new technique, which receives symmetric X-ray data as the input, is presented in this study by combining Convolutional Neural Networks (CNN) with Ant Lion Optimization Algorithm (ALO) and Multiclass Naïve Bayes Classifier (NB). Moreover, several other classifiers such as Softmax, Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and Decision Tree (DT) are combined with CNN. The promising results of these classifiers are evaluated and presented for accuracy, precision, and F1-score metrics. NB classifier with Ant Lion Optimization Algorithm and CNN produced the best results with 98.31% accuracy, 100% precision and 98.25% F1-score and with the lowest execution time.
dc.identifier.citationKarim, A. M., Kaya, H., Alcan, V., Sen, B., & Hadimlioglu, I. A. (2022). New Optimized Deep Learning Application for COVID-19 Detection in Chest X-ray Images. Symmetry, 14(5), 1003. https://doi.org/10.3390/sym14051003en_US
dc.identifier.citationKarim, A. M., Kaya, H., Alcan, V., Sen, B., & Hadimlioglu, I. A. (2022). New Optimized Deep Learning Application for COVID-19 Detection in Chest X-ray Images. Symmetry, 14(5), 1003. https://doi.org/10.3390/sym14051003
dc.identifier.doihttps://doi.org/10.3390/sym14051003
dc.identifier.urihttps://hdl.handle.net/1969.6/94039
dc.language.isoen_USen_US
dc.language.isoen_US
dc.rightsAttribution 4.0 International*
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectcovid-19en_US
dc.subjectdeep-learningen_US
dc.subjectcnnen_US
dc.subjectx-ray imagesen_US
dc.subjectdiagnosisen_US
dc.subjectcovid-19
dc.subjectdeep-learning
dc.subjectcnn
dc.subjectx-ray images
dc.subjectdiagnosis
dc.titleNew optimized deep learning application for COVID-19 detection in chest x-ray imagesen_US
dc.titleNew optimized deep learning application for COVID-19 detection in chest x-ray images
dc.typeArticleen_US
dc.typeArticle

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