Towards an Effective and Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Images

Confronting the pandemic of COVID-19 caused by the new coronavirus, the SARS-CoV-2, is nowadays one of the most prominent challenges of the human species. A key factor in slowing down the virus propagation is the rapid diagnosis and isolation of infected patients. Nevertheless, the standard method for COVID-19 identification, the Reverse transcription polymerase chain reaction (RT-PCR) method, is time-consuming and in short supply due to the pandemic. Researchers around the world have been looking for alternative screening methods. In this context, deep learning applied to chest X-rays of patients has been showing promising results in the identification of COVID-19. Despite their success, the computational cost of these methods remains high, which imposes difficulties in their accessibility and availability. Thus, in this work, we propose to explore and extend the EfficientNet family of models using chest X-rays images to perform COVID-19 detection. As a result, we can produce a high-quality model with an overall accuracy of 93.9%, COVID-19, sensitivity of 96.8% and positive prediction of 100% while having about 30 times fewer parameters than the baseline literature model, 28 and 5 times fewer parameters than the popular VGG16 and ResNet50 architectures, respectively. We believe the reported figures represent state-of-the-art results, both in terms of efficiency and effectiveness, for the COVIDx database, a database comprised of 13,800 X-ray images, 183 of which are from patients affected by COVID-19.