Pulmonary-Restricted COVID-19 Informative Visual Screening Using Chest X-ray Images from Portable Devices

Abstract

[Abstract]: In the recent COVID-19 outbreak, chest X-rays were the main tool for diagnosing and monitoring the pathology. To prevent further spread of this disease, special circuits had to be implemented in the healthcare services. For this reason, these chest X-rays were captured with portable X-ray devices that compensate its lower quality and limitations with more deployment flexibility. However, most of the proposed computer-aided diagnosis methodologies were designed to work with traditional fixed X-ray machines and their performance is diminished when faced with these portable images. Additionally, given that the equipment needed to properly treat the disease (such as for life support and monitoring of vital signs) most of these systems learnt to identify these artifacts in the images instead of real clinically-significant variables. In this work, we present the first methodology forced to extract features exclusively from the pulmonary region of interest that is specially designed to work with these difficult portable images. Additionally, we generate a class activation map so the methodology also provides explainability to the results returned to the clinician. To ensure the robustness of our proposal, we tested the methodology with chest radiographs from patients diagnosed with COVID-19, pathologies similar to COVID-19 (such as other types of viral pneumonias) and healthy patients in different combinations with three convolutional networks from the state of the art (for a total of 9 studied scenarios). The experimentation confirms that our proposal is able to separate COVID-19 cases, reaching a 94.7% ± 1.34% of accuracy.