Journal Article

COVID-19 Recognition Using Ensemble-CNNs in Two New Chest X-ray Databases

Taleb-Ahmed, Abdelmalik, Bougourzi, Fares, Vantaggiato, Edoardo, Hadid, Abdenour, Paladini, Emanuela, Distante, Cosimo

2021

Overview

The recognition of COVID-19 infection from X-ray images is an emerging field in the learning and computer vision community. Despite the great efforts that have been made in this field since the appearance of COVID-19 (2019), the field still suffers from two drawbacks. First, the number of available X-ray scans labeled as COVID-19-infected is relatively small. Second, all the works that have been carried out in the field are separate; there are no unified data, classes, and evaluation protocols. In this work, based on public and newly collected data, we propose two X-ray COVID-19 databases, which are three-class COVID-19 and five-class COVID-19 datasets. For both databases, we evaluate different deep learning architectures. Moreover, we propose an Ensemble-CNNs approach which outperforms the deep learning architectures and shows promising results in both databases. In other words, our proposed Ensemble-CNNs achieved a high performance in the recognition of COVID-19 infection, resulting in accuracies of 100% and 98.1% in the three-class and five-class scenarios, respectively. In addition, our approach achieved promising results in the overall recognition accuracy of 75.23% and 81.0% for the three-class and five-class scenarios, respectively. We make our databases of COVID-19 X-ray scans publicly available to encourage other researchers to use it as a benchmark for their studies and comparisons.

• Publisher: MDPI,MDPI AG
• Subject: Algorithms / Artificial Intelligence / Computer Science / convolutional neural network / COVID-19 / COVID-19 - diagnostic imaging / Deep Learning / Electronics / Engineering Sciences / Ensemble-CNNs / Humans / Networking and Internet Architecture / Neural Networks, Computer / Radiography, Thoracic / Signal and Image processing / X-ray scans / X-Rays