Karim, Ahmad M.Kaya, HilalAlcan, VeyselSen, BahaHadimlioglu, Ismail Alihan2022-09-202022-09-202022-05-14Karim, 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/sym14051003Karim, 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/sym14051003https://hdl.handle.net/1969.6/94039Due 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.Due 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-USAttribution 4.0 InternationalAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/http://creativecommons.org/licenses/by/4.0/covid-19deep-learningcnnx-ray imagesdiagnosiscovid-19deep-learningcnnx-ray imagesdiagnosisArticlehttps://doi.org/10.3390/sym14051003