A Novel Web Platform for COVID-19 diagnosis using X-Ray exams and Deep Learning Techniques

Abstract

Modern computer vision techniques applied to radiographic studies are presented as an alternative to assist the specialist in screening and diagnosing the respiratory syndrome (SARS-CoV-2), assisting in clinically severe cases, such as acute pneumonia, acute respiratory failure, organ failure, and death. This work proposes a screening method based on the Internet of Medical Things (IoMT) based on deep learning techniques for the classification of COVID-19 from chest X-ray (CXR) exams. The proposed system called Computer-Aided Remote medical diagnostics System (CARMEDSys) applied to the diagnosis of COVID-19 consists of three main stages: 1) segmentation of the lung region in X-ray images, 2) deep extraction of attributes from the filtered pulmonary area and 3) Prediction patient status with machine learning assistance. The performance of CARMEDSys was evaluated considering twelve different deep neural networks, via the transfer of learning. Besides, the performance of this approach is evaluated against recent studies for the classification of healthy patients, with pneumonia, or with COVID-19. The evaluation methodology considered two different sets of radiographic images, reaching Sensitivity (99.97%), F1-Score (99.43%), and Accuracy (98.89%) promising to distinguish patients with pneumonia and COVID-19 combining DenseNet201 as attribute extractor with Support Vector Machine with radial basis function, exceeding up to 12.31 % sensitivity for prediction of COVID-19 recent related works.