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

Abstract

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.

RRG-GAN Restoring Network for Simple Lens Imaging System

“…The computing time of a single image with 1920 × 1080 resolution is usually more than 5 min, which is unacceptable in industrial applications. With the continuous development of deep learning theory and GPU acceleration hardware technology, the restoring methods based on deep learning show great advantages in effect and performance [ 21 , 22 ]. The restoring method proposed in this paper is based on the deep learning theory and realizes the image restoration procedure in an end-to-end way.…”

Per-Covid-19: A Benchmark Database For Covid-19 Percentage Prediction From CT-scans

“…In the second scenario, we use the same models as the first scenario but this time they were trained on the recognition of Covid-19 from X-ray scans 2 . In more details, four lung diseases plus neutral were used to train the CNN architectures 2 .…”

“…To save the infected persons lives and stop the spread of Covid-19 disease, many methods have been used to recognize the infected persons. These methods include Reverse transcription polymerase chain reaction (RT-PCR) 1 , X-ray scan [2][3][4] and CT-scan 5,6 . Despite that the RT-PCR test is considered as the global standard method for Covid-19 diagnosis, this method has many downsides 7,8 .…”

“…In details, RT-PCR test is time consuming and has considerable False-Negative Rate 1 . Using X-ray scan and CT-scan methods can replace RT-PCR test and give an efficient result in both time and accuracy 2,8 . However, both these methods need an expert radiologist to identify Covid-19 infection.…”

Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN

“…To stop the spread of COVID-19, it is mandatory to recognize and then confine infected persons. Many recognition methods have proved their efficiency including RT-PCR, CT scans, and X-ray scans [3]. Despite the fact that the RT-PCR test is considered as the gold standard in diagnosing COVID-19, it has a considerable falsenegative rate, especially in early stages of infection [4].…”