Since the patient is not quarantined during the conclusion of the Polymerase Chain Reaction (PCR) test used in the diagnosis of COVID-19, the disease continues to spread. In this study, it was aimed to reduce the duration and amount of transmission of the disease by shortening the diagnosis time of COVID-19 patients with the use of Computed Tomography (CT). In addition, it is aimed to provide a decision support system to radiologists in the diagnosis of COVID-19. In this study, deep features were extracted with deep learning models such as ResNet-50, ResNet-101, AlexNet, Vgg-16, Vgg-19, GoogLeNet, SqueezeNet, Xception on 1345 CT images obtained from the radiography database of Siirt Education and Research Hospital. These deep features are given to classification methods such as Support Vector Machine (SVM), k Nearest Neighbor (kNN), Random Forest (RF), Decision Trees (DT), Naive Bayes (NB), and their performance is evaluated with test images. Accuracy value, F1-score and ROC curve were considered as success criteria. According to the data obtained as a result of the application, the best performance was obtained with ResNet-50 and SVM method. The accuracy was 96.296%, the F1-score was 95.868%, and the AUC value was 0.9821. The deep learning model and classification method examined in this study and found to be high performance can be used as an auxiliary decision support system by preventing unnecessary tests for COVID-19 disease.

由于在用于诊断 COVID-19 的聚合酶链反应 (PCR) 测试结束期间未对患者进行隔离，因此该疾病继续传播。在这项研究中，旨在通过使用计算机断层扫描 (CT) 缩短 COVID-19 患者的诊断时间来减少疾病传播的持续时间和数量。此外，它旨在为放射科医生诊断 COVID-19 提供决策支持系统。本研究利用 ResNet-50、ResNet-101、AlexNet、Vgg-16、Vgg-19、GoogLeNet、SqueezeNet、Xception 等深度学习模型对从 Siirt Education 和研究医院。这些深层特征被赋予支持向量机（SVM）、k 最近邻（kNN）等分类方法，随机森林 (RF)、决策树 (DT)、朴素贝叶斯 (NB)，并使用测试图像评估它们的性能。准确性值、F1 分数和 ROC 曲线被视为成功标准。根据应用结果获得的数据，使用 ResNet-50 和 SVM 方法获得了最佳性能。准确率为 96.296%，F1 分数为 95.868%，AUC 值为 0.9821。在本研究中检查并发现具有高性能的深度学习模型和分类方法可以用作辅助决策支持系统，以防止对 COVID-19 疾病进行不必要的测试。使用 ResNet-50 和 SVM 方法获得了最佳性能。准确率为 96.296%，F1 分数为 95.868%，AUC 值为 0.9821。在本研究中检查并发现具有高性能的深度学习模型和分类方法可以用作辅助决策支持系统，以防止对 COVID-19 疾病进行不必要的测试。使用 ResNet-50 和 SVM 方法获得了最佳性能。准确率为 96.296%，F1 分数为 95.868%，AUC 值为 0.9821。在本研究中检查并发现具有高性能的深度学习模型和分类方法可以用作辅助决策支持系统，以防止对 COVID-19 疾病进行不必要的测试。