Fully automatic deep convolutional approaches for the analysis of COVID-19 using chest X-ray images

Covid-19 is a new infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the seriousness of the situation, the World Health Organization declared a global pandemic as the Covid-19 rapidly around the world. Among its applications, chest X-ray images are frequently used for an early diagnostic/screening of Covid-19 disease, given the frequent pulmonary impact in the patients, critical issue to prevent further complications caused by this highly infectious disease. In this work, we propose 4 fully automatic approaches for the classification of chest X-ray images under the analysis of 3 different categories: Covid-19, pneumonia and healthy cases. Given the similarity between the pathological impact in the lungs between Covid-19 and pneumonia, mainly during the initial stages of both lung diseases, we performed an exhaustive study of differentiation considering different pathological scenarios. To address these classification tasks, we evaluated 6 representative state-of-the-art deep network architectures on 3 different public datasets: (I) Chest X-ray dataset of the Radiological Society of North America (RSNA); (II) Covid-19 Image Data Collection; (III) SIRM dataset of the Italian Society of Medical Radiology. To validate the designed approaches, several representative experiments were performed using 6,070 chest X-ray radiographs. In general, satisfactory results were obtained from the designed approaches, reaching a global accuracy values of 0.9706 +/- 0.0044, 0.9839 +/- 0.0102, 0.9744 +/- 0.0104 and 0.9744 +/- 0.0104, respectively, thus helping the work of clinicians in the diagnosis and consequently in the early treatment of this relevant pandemic pathology. (C) 2021 The Authors. Published by Elsevier B.V.

Automated summary

In this study, 4 fully automatic approaches were proposed for chest X-ray image classification into 3 categories: Covid-19, pneumonia, and healthy cases. Extensive differentiation analysis was conducted between Covid-19 and pneumonia due to their similar lung pathological impacts during early stages. Six representative deep network architectures were evaluated on 3 public datasets to achieve global accuracy values benefiting clinicians in the diagnosis and early treatment of Covid-19.