Covid-19 Chest Radiography Images Analysis Based on Integration of Image Preprocess, Guided Grad-CAM, Machine Learning and Risk Management

COVID19 coronavirus has widely infected more than 10 million people and killed more than 500,000 globally till July 1, 2020. In this paper, we describe a potential methodology, integration of image preprocess, Guided Grad-CAM, machine learning and risk management based on chest radiography images, as one of workable alarm and analysis systems to support clinicians against COVID-19 outbreak threat. We leverage pre-trained CNN models as backbone with further transfer learning to analyze public open datasets composed of 5851 chest radiography images for 4 classes classification, and 15478 images from COVIDx dataset for 3 classes classification, facilitated with steps of ROI and mask, and CNN layer visualization of guided grad-CAM to help CNN focused on critical infection focus in qualitative perspective. In quantitative perspective of 4 classes classification result, accuracy, average sensitivity, average precision, and COVID19 sensitivity of single ResNet50 and our second bagging ensemble model are (77.2%/78.8%/81.9%/100%) and (81.5%/81.4%,86.8%/100%) respectively. Ensemble way of several CNNs and other machine learning methods used here is to contribute about 4% accuracy improvement on top of best single CNN (ResNet50). In our 3 classes classification, those metrics of ensemble model and benchmark are (93.1%/90.1%/89.7%/83%) and (90%/85.9%, 82.4%/77%). We conclude ensemble approach would facilitate weaker classifier more. Beside to accuracy-oriented analysis, a cost minimization approach is suggested here to provide clinicians options of different risk consideration flexibility by trade off among different categories and performance rates.