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

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 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) labeled by the medical doctors and 12544 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.

Keywords: COVID-19 detection in early stages; deep learning; machine learning; representation based classification.

FIGURE 1.

(first row) Samples of COVID-19 pneumonia with very limited or no visible sign of COVID-19, and (second row) normal (healthy) class from Early-QaTa-COV19 dataset.

FIGURE 2.

Sample X-ray images from Early-QaTa-COV19 in different quality, resolution and noise level and showing no or very limited sign of COVID-19 pneumonia.

FIGURE 3.

Feature extraction pipeline from the pre-trained DenseNet-121. 1024-D feature vectors are extracted for the compact classifiers trained for the early detection of COVID-19.

FIGURE 4.

Representation-based classification pipeline for the early detection of COVID-19 using chest X-ray images.

FIGURE 5.

The CSEN approach for the early COVID-19 detection from chest X-ray images.

FIGURE 6.

MLP framework used for the early detection of COVID-19.

FIGURE 7.

False negatives of the CSEN1 and DenseNet-121 * which is initialized with ChestX-ray14 weights and fine-tuned on Initial Early-QaTa-COV19.

FIGURE 8.

Time complexity versus the sensitivity of all the evaluated classifiers. Computational times are plotted in log-scale and measured for the evaluation of test sets by averaging over 5-folds.