Coronavirus Diagnosis Based on Chest X-Ray Images and Pre-Trained DenseNet-121

Abstract

A serious global problem called COVID-19 has killed a great number of people and rendered many projects useless. The obtained individual's identification at the appropriate time is one of the crucial methods to reduce losses. By detecting and recognizing contaminated individuals in the early stages, artificial intelligence can help many associations in these situations. In this study, we offer a fully automated method to identify COVID-19 from a patient's chest X-ray images without the need for a clinical expert's assistance. The proposed approach was evaluated on the public COVID-19 X-ray dataset that achieves high performance and reduces computational complexity. This dataset contains 400 photos, 100 images of individuals who were infected with Covid-19, 100 images of individuals with no COVID-19, 100 images of a viral pneumonia and a 100 more images that we reserve them for testing part. So we have an overall 300 images for training and 100 for testing. The obtained results were so satisfying, an F1 score of 0.98, a Recall of 0.98, and an Accuracy of 0.98. The classification method deep learning-based DenseNet-121, transfer learning, as well as data augmentation techniques were implemented to improve the model more accurately. Our proposed approach outperforms several CNNs and all recent works on COVID‑19 images. Even though there are not enough training photos comparing to other extra-large datasets.