COVID-19 Detection via Image Classification using Deep Learning on Chest X-Ray

The focus of this study is to evaluate and examine a set of deep learning transfer learning techniques applied to chest radiograph images for the classification of COVID-19, normal (healthy), and pneumonia. In this work, we have used four transfer learning models, VGG16, InceptionV3, ResNet50, and DenseNet121 for the classification tasks. Our results indicate that the VGG16 method outperforms comparative classification models in terms of accuracy, sensitivity, and specificity. The VGG16 model detects and classifies COVID-19, normal (healthy), and pneumonia with 94% test accuracy, 94% sensitivity, and 94.20% specificity. Code is publically available at: https://github.com/ayyaz-azeem/Covid19challenge.git