TY  - EJOU
AU  - Faisal, Mohammed 
AU  - Albogamy, Fahad 
AU  - ElGibreen, Hebah 
AU  - Algabri, Mohammed 
AU  - Alvi, Syed Ahad M. 
AU  - Alsulaiman, Mansour 

TI  - COVID-19 Diagnosis Using Transfer-Learning Techniques
T2  - Intelligent Automation \& Soft Computing

PY  - 2021
VL  - 29
IS  - 3
SN  - 2326-005X

AB  - 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.
KW  - Covid-19; deep learning; diagnosis system; VGG-19; NASNet; MobileNet2

DO  - 10.32604/iasc.2021.017898