Automatic Detection of COVID-19 from Chest X-ray Images Using Deep Learning Model

TL;DR

This paper presents a deep learning-based method for automatic COVID-19 detection from chest X-ray images, aiming to address testing limitations and improve diagnostic speed and accuracy.

Contribution

It introduces a novel deep learning approach for COVID-19 detection from X-ray images and evaluates its effectiveness on publicly available datasets.

Findings

Models show satisfactory performance

Achieves promising accuracy compared to existing methods

Demonstrates potential for rapid, automated diagnosis

Abstract

The infectious disease caused by novel corona virus (2019-nCoV) has been widely spreading since last year and has shaken the entire world. It has caused an unprecedented effect on daily life, global economy and public health. Hence this disease detection has life-saving importance for both patients as well as doctors. Due to limited test kits, it is also a daunting task to test every patient with severe respiratory problems using conventional techniques (RT-PCR). Thus implementing an automatic diagnosis system is urgently required to overcome the scarcity problem of Covid-19 test kits at hospital, health care systems. The diagnostic approach is mainly classified into two categories-laboratory based and Chest radiography approach. In this paper, a novel approach for computerized corona virus (2019-nCoV) detection from lung x-ray images is presented. Here, we propose models using deep learning to show the effectiveness of diagnostic systems. In the experimental result, we evaluate proposed models on publicly available data-set which exhibit satisfactory performance and promising results compared with other previous existing methods.