Transfer Learning with Deep Convolutional Neural Networks for the Detection of COVID-19 Disease Using Lung Computed Tomography Images

Background/Objectives: In order to prevent the transmission rate of COVID-19, early diagnosis with high accuracy is essential. The aim of this study is to identify the disease using convolutional neural network (CNN) architectures that allow accurate and rapid diagnosis of COVID-19 pneumonia on computed tomography (CT) images and to evaluate the classification success of the architectures by comparing their classification success with various performance metrics. Methods: In the study dataset, a total of 15584 lung CT slices were obtained, 8395 slices from 361 positive cases and 7189 slices from 134 negative cases, selected in accordance with the study criteria, with RT-PCR test results and CT scans. The dataset were analysed using fine-tuned DenseNet169, MobileNetV2, ResNet50, InceptionResNetV2, InceptionV3, VGG16, VGG19, Xception, DenseNet121, DenseNet201 and ResNet101. Accuracy, sensitivity, specificity, precision, F1 score, ROC curve and AUC were used for performance evaluation. Results: In the SARS-CoV-2 CT scan dataset and the study dataset, the highest performance metrics were obtained from the fine-tuned DenseNet201 with accuracy of 99.19%, sensitivity of 98.65%, specificity of 99.64%, precision of 99.55%, F1 score of 99.10%, AUC of 99.14%, and accuracy of 99.13%, sensitivity of 99.86%, specificity of 98.52%, precision of 98.28%, F1 score of 99.07%, AUC of 99.19%, respectively. The other highest accuracies for the study dataset were 97.47% with fine-tuned DenseNet169 and 97.85% with fine-tuned DenseNet121. Conclusions: As a result, the fine-tuned DenseNet201 proposed in this study shows a promising performance on lung CT images and two different datasets.