@Article{cmes.2020.011920,
AUTHOR = {Xing Deng, Haijian Shao, Liang Shi, Xia Wang, Tongling Xie},
TITLE = {A Classification–Detection Approach of COVID-19 Based on Chest X-ray and CT by Using Keras Pre-Trained Deep Learning Models},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {125},
YEAR = {2020},
NUMBER = {2},
PAGES = {579--596},
URL = {http://www.techscience.com/CMES/v125n2/40310},
ISSN = {1526-1506},
ABSTRACT = {The Coronavirus Disease 2019 (COVID-19) is wreaking havoc
around the world, bring out that the enormous pressure on national health
and medical staff systems. One of the most effective and critical steps in
the fight against COVID-19, is to examine the patient’s lungs based on the
Chest X-ray and CT generated by radiation imaging. In this paper, five
keras-related deep learning models: ResNet50, InceptionResNetV2, Xception, transfer learning and pre-trained VGGNet16 is applied to formulate an
classification–detection approaches of COVID-19. Two benchmark methods
SVM (Support Vector Machine), CNN (Convolutional Neural Networks) are
provided to compare with the classification–detection approaches based on
the performance indicators, i.e., precision, recall, F1 scores, confusion matrix,
classification accuracy and three types of AUC (Area Under Curve). The
highest classification accuracy derived by classification–detection based on
5857 Chest X-rays and 767 Chest CTs are respectively 84% and 75%, which
shows that the keras-related deep learning approaches facilitate accurate and
effective COVID-19-assisted detection.},
DOI = {10.32604/cmes.2020.011920}
}