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Confronting the pandemic of COVID-19, is nowadays one of the most prominentchallenges of the human species. A key factor in slowing down the viruspropagation is the rapid diagnosis and isolation of infected patients. Thestandard method for COVID-19 identification, the Reverse transcriptionpolymerase chain reaction method, is time-consuming and in short supply due tothe pandemic. Thus, researchers have been looking for alternative screeningmethods and deep learning applied to chest X-rays of patients has been showingpromising results. Despite their success, the computational cost of thesemethods remains high, which imposes difficulties to their accessibility andavailability. Thus, the main goal of this work is to propose an accurate yetefficient method in terms of memory and processing time for the problem ofCOVID-19 screening in chest X-rays. Methods: To achieve the defined objectivewe exploit and extend the EfficientNet family of deep artificial neuralnetworks which are known for their high accuracy and low footprints in otherapplications. We also exploit the underlying taxonomy of the problem with ahierarchical classifier. A dataset of 13,569 X-ray images divided into healthy,non-COVID-19 pneumonia, and COVID-19 patients is used to train the proposedapproaches and other 5 competing architectures. Finally, 231 images of thethree classes were used to assess the quality of the methods. Results: Theresults show that the proposed approach was able to produce a high-qualitymodel, with an overall accuracy of 93.9%, COVID-19, sensitivity of 96.8% andpositive prediction of 100%, while having from 5 to 30 times fewer parametersthan other than the other tested architectures. Larger and more heterogeneousdatabases are still needed for validation before claiming that deep learningcan assist physicians in the task of detecting COVID-19 in X-ray images.

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