COVID-19 Diagnosis Using Transfer-Learning Techniques

COVID-19 was first discovered in Wuhan, China, December 2019 and has since spread worldwide. An automated fast diagnosis system needs to be developed for early effective diagnosis. Hence, we propose two- three-classifier systems classifying cases using transfer-learning techniques. These can classify X-ray images into three categories: healthy, COVID-19, pneumonia cases. We used two image datasets (DATASET-1 DATASET-2) collected from state-of-the-art studies train the deep learning architectures, such as VGG-19, NASNet, MobileNet2, on these datasets. According validation testing results, our proposed achieved excellent results with VGG-19 architecture. The two-classifier high sensitivity 99.5% 100% DATASET-1 DATASET-2, respectively. achieves 98.4% of diagnostic will significantly improve speed precision