Covid-19 diagnosis using transfer-learning techniques
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.
The present invention relates to the intelligent and secure IoT response to a patient and, more specifically, to a ventilator monitor system that receives a variety of fan supports signals showing…
Detection and prediction of infectious disease is a very challenging task due to the lack of substantial evidence of the disease and its behaviours. The effective infection prevention mechanisms…
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…