Accurate Detection of COVID-19 Using K-EfficientNet Deep Learning Image Classifier and K-COVID Chest X-Ray Images Dataset

Abstract

Our present work allows efficient detection of COVID-19 from chest X-Rays at a level exceeding practicing radiologists. The algorithm uses the architecture EfficientNet extended and named K-EfficientNet. The K-EfficientNet is associated with progressive resizing, which resizes the images from 112× 112 to 224× 224 during the training process. By combining six publicly available and independent datasets, we get a large dataset named K-COVID containing 14,124 X-Rays images of patients affected by Pneumonia or COVID-19 and patient with Normal X-Ray images. The application of transfer learning on the ImageNet dataset and data augmentation allows us to achieve 97.3% accuracy and 100% sensitivity, and 100% Positive Predictive Value on COVID-19 detection.