New Optimized Deep Learning Application for COVID-19 Detection in Chest X-ray Images

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dc.authoridAlcan, Veysel/0000-0002-7786-8591
dc.authoridKarim, Ahmad M./0000-0003-4359-6628
dc.authoridSEN, BAHA/0000-0003-3577-2548
dc.authoridHadimlioglu, Ismail Alihan/0000-0003-1588-1245
dc.authoridKaya, Hilal/0000-0003-4787-105X
dc.contributor.authorKarim, Ahmad Mozaffer
dc.contributor.authorKaya, Hilal
dc.contributor.authorAlcan, Veysel
dc.contributor.authorSen, Baha
dc.contributor.authorHadimlioglu, Ismail Alihan
dc.date.accessioned2025-03-17T12:25:12Z
dc.date.available2025-03-17T12:25:12Z
dc.date.issued2022
dc.departmentTarsus Üniversitesi
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 Naive 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.doi10.3390/sym14051003
dc.identifier.issn2073-8994
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85130694042
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.3390/sym14051003
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1556
dc.identifier.volume14
dc.identifier.wosWOS:000801903600001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSymmetry-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250316
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 Images
dc.typeArticle

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