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

TL;DR

This paper introduces a novel convolutional support estimator for Covid-19 detection from X-ray images, aiming to improve accuracy and speed with limited datasets by bridging model-based and deep learning methods.

Contribution

The study proposes a convolutional support estimation network (CSEN) that enhances Covid-19 recognition accuracy from X-ray images, especially with small datasets, by combining model-based and deep learning approaches.

Findings

The proposed CSEN achieves real-time Covid-19 detection from X-ray images.

It outperforms traditional representation-based methods in speed and accuracy.

The approach is effective even with limited training data.

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 Transcription-Polymerase 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 X-ray 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.