Detection of Covid-19 in Chest X-Ray Images using Deep Convolutional Neural Network

Abstract

Abstract: The pandemic COVID -19 has created an urgent need to do our part in the fight against the great threat to humanity. Studies diagnosing COVID -19 on chest x-rays havea binary or multiclass classification. VGG-16 (Visual Geometry Group), The software, which uses a deep convolutional neural network to detect COVID -19 cases on chest x-ray images, is open source and available to download. This research makes a significant contribution to the healthcare industry and research community by proposing a synthetic data augmentation in chest X-ray images using a deep convolutional neural network (CNN) architecture for the detection of normal pneumonia and covid-19. The model used is VGG-16, and after training and validation, an average validation accuracy and loss of 0.97 and 0.026 were obtained, with specificity and sensitivity of 0.96 and 0.95, respectively, compared with previous models trained for binary classification to detect abnormalities such as pneumonia and covid-19 in radiographs.