A comparison of deep machine learning algorithms in COVID-19 disease diagnosis

TL;DR

This paper compares various deep machine learning algorithms for diagnosing COVID-19 from chest X-ray images, aiming to find accurate, cost-effective, and rapid detection methods to control the pandemic.

Contribution

It evaluates and compares multiple deep learning models specifically for COVID-19 detection using chest X-ray images, highlighting their effectiveness.

Findings

Deep learning models show high accuracy in COVID-19 detection from X-ray images.

Some algorithms outperform traditional diagnostic methods in speed and cost.

The study identifies the most effective deep learning techniques for this application.

Abstract

The aim of the work is to use deep neural network models for solving the problem of image recognition. These days, every human being is threatened by a harmful coronavirus disease, also called COVID-19 disease. The spread of coronavirus affects the economy of many countries in the world. To find COVID-19 patients early is very essential to avoid the spread and harm to society. Pathological tests and Chromatography(CT) scans are helpful for the diagnosis of COVID-19. However, these tests are having drawbacks such as a large number of false positives, and cost of these tests are so expensive. Hence, it requires finding an easy, accurate, and less expensive way for the detection of the harmful COVID-19 disease. Chest-x-ray can be useful for the detection of this disease. Therefore, in this work chest, x-ray images are used for the diagnosis of suspected COVID-19 patients using modern machine learning techniques. The analysis of the results is carried out and conclusions are made about the effectiveness of deep machine learning algorithms in image recognition problems.