Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images

Abstract

Coronavirus disease (Covid-19) has been the main agenda of the whole world since it came in sight in December, 2019. It has already caused thousands of causalities and infected several millions worldwide. Any technological tool that can be provided to healthcare practitioners to save time, effort, and possibly lives has crucial importance. The main tools practitioners currently use to diagnose Covid-19 are Reverse transcriptionpolymerase chain reaction (RT-PCR) and Computed Tomography (CT), which require significant time, resources and acknowledged experts. X-ray imaging is a common and easily accessible tool that has great potential for Covid-19 diagnosis. In this study, we propose a novel approach for Covid-19 recognition from chest Xray images. Despite the importance of the problem recent studies in this domain produced not so satisfactory results due to the limited datasets available for training. Recall that Deep Learning techniques can generally provide state-of-the-art performance in many classification tasks when trained properly over large datasets, such data scarcity can be a crucial obstacle when using them for Covid-19 detection. Alternative approaches such as representation-based classification (collaborative or sparse representation) might provide satisfactory performance with limited size datasets, but they generally fall short in performance or speed compared to Machine Learning methods. To address this deficiency, Convolution Support Estimation Network (CSEN) has recently been proposed as a bridge between model-based and Deep Learning approaches by providing a non-iterative real-time mapping from query sample to ideally sparse representation coefficient' support, which is critical information for class decision in representation based techniques.Main premises of this study can be summarized as follows: (i) a benchmark X-ray dataset, namely QaTa-Cov19, containing over 6200 X-ray images is created. Up to date, this is the largest dataset covering 462 X-ray images from Covid-19 patients along with three other classes; bacterial pneumonia, viral pneumonia, and normal. (ii) In such a scarce and imbalanced dataset the proposed CSEN based classification scheme equipped with feature extraction from a state-of-the-art deep neural network solution for X-ray images, CheXNet, achieves over 98% sensitivity and over 95% specificity for Covid-19 recognition directly from raw X-ray images without any pre-or post-processing. (iii) Having such an elegant Covid-19 assistive diagnosis performance, this study further provides solid evidence that Covid-19 induces a unique pattern in X-rays that can be discriminated with a high accuracy.

Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images

“…Chowdhury et al [24] proposed an ensemble of deep CNN models named as Efficient Convolutional Network (ECOVNet) to detect and classify COVID-19, normal and pneumonia using 16,493 CXR and achieved an accuracy of 97 %. Yamac et al [25] introduced a compact CNN architecture, Convolution Support Estimation Network (CSEN) that utilizes CheXNet as a feature extractor to classify the target CXR images as COVID-19, Bacterial pneumonia, Viral Pneumonia or Normal. The network produced 98% COVID-19 detection sensitivity using a dataset of 462 COVID-19 CXR images.…”

COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning

“…Other researchers have also tried to tackle the same problem using CT images, reaching high scoring metrics and precise abnormality localization [24,25]. Contrarily, even though many studies have claimed to reach excellent classification accuracy scores using CXRs [26,27,28,29,30,31,32], none of them have reported visualization results for their model decisions. Considering the fact that pneumonia diagnosis is more challenging in CXRs in comparison with CT scans and the available COVID-19 pneumonia CXR datasets are small, we investigate those studies with visual interpretability as it could be considered as a stronger performance metric.…”

COVID-19 infection map generation and detection from chest X-ray images

“…Such limited amount of data degrades the learning performance of the deep networks. Two recent studies [28] and [29] have addressed this drawback with a compact network structure and achieved the state-of-theart detection performance over the benchmark QaTa-COV19 and Early-QaTa-COV19 datasets that consist of 462 and 175 COVID-19 CXR images, respectively. Despite the fact that these datasets were the largest available at that time, such a limited number of COVID-19 samples raises robustness and reliability issues for the proposed methods in general.…”

“…In this study, in order to overcome the aforementioned limitations and drawbacks, first, the benchmark dataset QaTa-COVSeg proposed by the researchers of Qatar University and Tampere University in [28] and [29] is extended to include 2951 COVID-19 samples. This new dataset is 3-20 times larger than those used in earlier studies.…”