The main purpose of this work is to investigate and compare several deep learning enhanced techniques applied to X-ray and CT-scan medical images for the detection of COVID-19. In this paper, we used four powerful pre-trained CNN models, VGG16, DenseNet121, ResNet50,and ResNet152, for the COVID-19 CT-scan binary classification task. The proposed Fast.AI ResNet framework was designed to find out the best architecture, pre-processing, and training parameters for the models largely automatically. The accuracy and F1-score were both above 96% in the diagnosis of COVID-19 using CT-scan images. In addition, we applied transfer learning techniques to overcome the insufficient data and to improve the training time. The binary and multi-class classification of X-ray images tasks were performed by utilizing enhanced VGG16 deep transfer learning architecture. High accuracy of 99% was achieved by enhanced VGG16 in the detection of X-ray images from COVID-19 and pneumonia. The accuracy and validity of the algorithms were assessed on X-ray and CT-scan well-known public datasets. The proposed methods have better results for COVID-19 diagnosis than other related in literature. In our opinion, our work can help virologists and radiologists to make a better and faster diagnosis in the struggle against the outbreak of COVID-19.
【저자키워드】 Respiratory tract diseases, imaging, Computer science, 【초록키워드】 COVID-19, deep learning, Pneumonia, Diagnosis, X-ray, CNN, Accuracy, outbreak, Algorithm, COVID-19 diagnosis, Validity, CT-scan, best, Radiologists, Algorithms, help, transfer, datasets, high accuracy, radiologist, parameter, binary classification, IMPROVE, performed, addition, applied, faster, overcome, automatically, diagnosis of COVID-19, were assessed, 【제목키워드】 COVID-19, deep learning, detection, Analysis,