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

• Main Authors: Yadav, Samir S, Sandhu, Jasminder Kaur, Bendre, Mininath R, Vikhe, Pratap S, Kaur, Amandeep
• Format: Journal Article
• Language: English
• Published: 25-08-2020
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning
• DOI: 10.48550/arxiv.2008.11639

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.