2026-10-23T17:48:20Zhttps://gumushane-rdlopen.rawdatalibrary.net/oai/request

oai:gumushane-rdlopen.rawdatalibrary.net:gumushane/114222026-10-22T11:34:49Zopenairedrivercom_gumushane_10000col_gumushane_10001com_gumushane_10007col_gumushane_10171

Automatic prediction of covid-19 from chest-computed tomography (CT) images using deep learning architectures; [Derin öğrenme mimarilerini kullanarak göğüs BT görüntülerinden otomatik Covid-19 tahmini]Turk, VeyselCatal Reis, HaticeKaya, SerhatCOVID-19Deep learningLeNet-5MobileNetVGG16VGG19Machine learning has been actively used in disease detection and segmentation in recent years. For the last few years, the world has been coping with the Coronavirus disease 2019 (COVID-19) pandemic. Chest-computerized tomography (CT) is often a meaningful way to detect and detect patients with possible COVID-19. This study aims to classify COVID-19 and non-COVID-19 chest-CT images using deep learning (DL) algorithms and investigate whether we can achieve successful results in different parameters using four architectures. The study was performed on proved positive COVID-19 CT images, and the datasets were obtained from the GitHub public platform. The study evaluated four different deep learning architectures of VGG16, VGG19, LeNet-5, and MobileNet. The performance evaluations were used with ROC curve, recall, accuracy, F1-score, precision, and Root Mean Square Error (RMSE). MobileNet model showed the best result ; F1 score of 95%, the accuracy of 95%, the precision of 100%, recall of 90%, AUC of 95%, and RMSE of 0.23. On the other hand, VGG 19 model gave the lowest performance; F1 score of 90%, the accuracy of 89%, the precision of 90%, recall of 90%, AUC of 89%, and RMSE of 0.32. When the algorithms' performances were compared, the highest accuracy was obtained from MobileNet, LeNet-5, VGG16, and VGG19, respectively. This study has proven the usefulness of deep learning models to detect COVID-19 in chest-CT images based on the proposed model framework. Therefore, it can contribute to the literature in Medical and Engineering in COVID-19 detection research.Gumushane University2022Articlehttps://rdlopen.rawdatalibrary.net/r/gumushane/11422https://doi.org/10.17714/gumusfenbil.10027382146-538XenGumushane Universitesi Fen Bilimleri Dergisiinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://gumushane-rdlopen.rawdatalibrary.net/tr/items/gumushane/11422