@Article{iasc.2021.017898,
AUTHOR = {Mohammed Faisal, Fahad Albogamy, Hebah ElGibreen, Mohammed Algabri, Syed Ahad M. Alvi, Mansour Alsulaiman},
TITLE = {COVID-19 Diagnosis Using Transfer-Learning Techniques},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {29},
YEAR = {2021},
NUMBER = {3},
PAGES = {649--667},
URL = {http://www.techscience.com/iasc/v29n3/43041},
ISSN = {2326-005X},
ABSTRACT = {COVID-19 was first discovered in Wuhan, China, in December 2019 and has since spread worldwide. An automated and fast diagnosis system needs to be developed for early and effective COVID-19 diagnosis. Hence, we propose two- and three-classifier diagnosis systems for classifying COVID-19 cases using transfer-learning techniques. These systems can classify X-ray images into three categories: healthy, COVID-19, and pneumonia cases. We used two X-ray image datasets (DATASET-1 and DATASET-2) collected from state-of-the-art studies and train the systems using deep learning architectures, such as VGG-19, NASNet, and MobileNet2, on these datasets. According to the validation and testing results, our proposed diagnosis systems achieved excellent results with the VGG-19 architecture. The two-classifier diagnosis system achieved high sensitivity for COVID-19, with 99.5% and 100% on DATASET-1 and DATASET-2, respectively. The three-classifier diagnosis system achieves high sensitivity for COVID-19, with 98.4% and 100% on DATASET-1 and DATASET-2, respectively. The high sensitivity of these diagnostic systems for COVID-19 will significantly improve the speed and precision of COVID-19 diagnosis.},
DOI = {10.32604/iasc.2021.017898}
}