Log In Sign Up

Reliable COVID-19 Detection Using Chest X-ray Images

by   Aysen Degerli, et al.

Coronavirus disease 2019 (COVID-19) has emerged the need for computer-aided diagnosis with automatic, accurate, and fast algorithms. Recent studies have applied Machine Learning algorithms for COVID-19 diagnosis over chest X-ray (CXR) images. However, the data scarcity in these studies prevents a reliable evaluation with the potential of overfitting and limits the performance of deep networks. Moreover, these networks can discriminate COVID-19 pneumonia usually from healthy subjects only or occasionally, from limited pneumonia types. Thus, there is a need for a robust and accurate COVID-19 detector evaluated over a large CXR dataset. To address this need, in this study, we propose a reliable COVID-19 detection network: ReCovNet, which can discriminate COVID-19 pneumonia from 14 different thoracic diseases and healthy subjects. To accomplish this, we have compiled the largest COVID-19 CXR dataset: QaTa-COV19 with 124,616 images including 4603 COVID-19 samples. The proposed ReCovNet achieved a detection performance with 98.57


page 2

page 4


OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images

Coronavirus disease 2019 (COVID-19) has been diagnosed automatically usi...

Machine learning approaches for COVID-19 detection from chest X-ray imaging: A Systematic Review

There is a necessity to develop affordable, and reliable diagnostic tool...

A Comparative Study on Early Detection of COVID-19 from Chest X-Ray Images

In this study, our first aim is to evaluate the ability of recent state-...

Deep learning for COVID-19 diagnosis based feature selection using binary differential evolution algorithm

The new Coronavirus is spreading rapidly and it has taken the lives of m...

Objective Study of Sensor Relevance for Automatic Cough Detection

The development of a system for the automatic, objective and reliable de...