Convolutional Neural Network for Predicting COVID-19 from Chest x-ray Images

The world has recently witnessed many deaths for all age groups due to the respiratory COVID-19 but detecting this disease in its early stages helps to recover, avoid negative effects, and reduce the outbreak of the disease quickly. Many symptoms of this disease were found, most notably chest infections and shortness of breath resulting from infection with this disease. The goal of this project is to use chest x-rays images to predict whether a person has the COVID-19 or not. In this study, we tested the solution performances for our problem on different versions of the CNN. Such as Mobile Net, CNN with Adam optimizer, CNN with Data Augmentation, CNN with Batch Normalization, CNN with Leaky Relu, CNN with Dropout, CNN with Early Stopping, CNN with Hyper-parameter Tuning, RESNET50, VGG-16, and VGG-19. The results showed that the VGG-19 model outperformed all the models in detecting infection with MERS-Cove quickly and with high accuracy instead of regular examinations that take a long time and thus limit the spread of the disease.