The severe damage caused by COVID-19 has become a reality, and there is no longer a way to save humanity from this epidemic except diagnose and prevention, especially with emergence delay and lack of vaccine recognized by the World Health Organization Without therapeutic treatment or explicit restorative immunizations for COVID-19, it is fundamental to diagnose the disease at an early stage and quickly seclude patients contaminated with the virus This study aims at estimating the damage via consistency of chest imaging, which is not always feasible or possible Here, an application is proposed to solve the problem via a WEB Predictor ‘COVIDz” and a program exploiting deep learning, so as emergency care will be able to systematically bring chest X-ray images and predict the percentage of the absence or presence of COVID-19 The proposed approach (custom VGG model) and our WEB site “COVIDz” objective validation of the suggested solution obtained the best classification efficiency of 99 64%, F-score of 99 2%, precision of 99 28%, MCC of 99 28%, recall of 99 28%, and a specificity value of 100% © Springer Nature Switzerland AG 2021
COVIDz: Deep learning for coronavirus disease detection