Chest X-ray Image Classification for COVID-19 diagnoses

Background: Radiologists used chest radiographs to detect coronavirus disease 2019 (COVID-19) in patients and determine the severity levels. The COVID-19 cases were grouped into five classes, each receiving different treatments. An intelligent system is needed advance detection identify vector features of X-ray images with a quality that too poor be read by radiologists. Deep learning an can this case. Objective: current study compares classification accuracy methods two, three dan classes. Methods: classify visual geometry group VGG 19 architectures 1000 classes' convolutional neural network (CNN) underwent model validation confusion matrix produce class values. could then diagnose patients’ examinations radiology specialists. Results: results five-class method showed 98% accuracy, three-class 99.99%, two-class 99.99%. Conclusion: It concluded using effective. This viruses assist radiologists reading images. Keywords: COVID-19, CNN, Classification, Learning