A deep learning based COVID 19 automatic diagnostic framework using chest X ray images
The lethal novel coronavirus disease 2019 (COVID 19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID 19 as a quick alternative diagnostic option to control its spread. A robust deep learning based system is proposed to detect the COVID 19 using chest X ray images. Infected patient's chest X ray images reveal numerous opacities (denser, confluent, and more profuse) in comparison to healthy lungs images which are used by a deep learning algorithm to generate a model to facilitate an accurate diagnostics for multi class classification (COVID vs. normal vs. bacterial pneumonia vs. viral pneumonia) and binary classification (COVID 19 vs. non COVID) COVID 19 positive images have been used for training and model performance assessment from several hospitals of India and also from countries like Australia, Belgium, Canada, China, Egypt, Germany, Iran, Israel, Italy, Korea, Spain, Taiwan, USA, and Vietnam. The data were divided into training, validation and test sets. The average test accuracy of 97.11 2.71% was achieved for multi class (COVID vs. normal vs. pneumonia) and 99.81% for binary classification (COVID 19 vs. non COVID) The proposed model performs rapid disease detection in 0.137 s per image in a system equipped with a GPU and can reduce the workload of radiologists by classifying thousands of images on a single click to generate a probabilistic report in real time.
Biocybernetics and Biomedical Engineering