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G. I. Okolo, S. Katsigiannis, T. Althobaiti, N. Ramzan, N. “On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays”. Sensors 2021, 21, 5702.

This research work examines eleven deep convolutional neural network architectures for the task of classifying chest X-ray images as belonging to healthy individuals, individuals with COVID-19 or individuals with viral pneumonia. All the examined networks are established architectures that have been proven to be efficient in image classification tasks, and we evaluated three different adjustments to modify the architectures for the task at hand by expanding them with additional layers. The proposed approaches were evaluated for all the examined architectures on a dataset with real chest X-ray images, reaching the highest classification accuracy of 98.04% and the highest F1-score of 98.22% for the best-performing setting.