Advance Warning Methodologies for COVID-19 using Chest X-Ray Images

TL;DR

This study evaluates machine learning methods, including a novel compact classifier, for early COVID-19 detection from chest X-ray images, and introduces a new benchmark dataset for this purpose.

Contribution

It proposes the CSEN classifier for scarce-data COVID-19 detection and introduces the Early-QaTa-COV19 dataset with early-stage samples.

Findings

CSEN achieves over 97% sensitivity and 95.5% specificity.

DenseNet-121 attains 95% sensitivity and 99.74% specificity.

The new dataset contains 1065 early-stage COVID-19 samples labeled by doctors.

Abstract

Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage. In this study, our first aim is to evaluate the ability of recent \textit{state-of-the-art} Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Both compact classifiers and deep learning approaches are considered in this study. Furthermore, we propose a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task. Finally, this study introduces a new benchmark dataset called Early-QaTa-COV19, which consists of 1065 early-stage COVID-19 pneumonia samples (very limited or no infection signs) labelled by the medical doctors and 12 544 samples for control (normal) class. A detailed set of experiments shows that the CSEN achieves the top (over 97%) sensitivity with over 95.5% specificity. Moreover, DenseNet-121 network produces the leading performance among other deep networks with 95% sensitivity and 99.74% specificity.