On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays

The global COVID-19 pandemic that started in 2019 and created major disruptions around the world demonstrated imperative need for quick, inexpensive, accessible reliable diagnostic methods would allow detection of infected individuals with minimal resources. Radiography, more specifically, chest radiography, is a relatively inexpensive medical imaging modality can potentially offer solution diagnosis cases. In this work, we examined eleven deep convolutional neural network architectures task classifying X-ray images as belonging to healthy individuals, or viral pneumonia. All networks are established have been proven be efficient image classification tasks, evaluated three different adjustments modify at hand by expanding them additional layers. proposed approaches were all on dataset real images, reaching highest accuracy 98.04% F1-score 98.22% best-performing setting.