A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images

• Published in: Interdisciplinary sciences : computational life sciences Vol. 13; no. 1; pp. 103 - 117
• Main Authors: Rasheed, Jawad, Hameed, Alaa Ali, Djeddi, Chawki, Jamil, Akhtar, Al-Turjman, Fadi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2021; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial neural networks; Biomedical and Life Sciences; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Appl. in Life Sciences; Coronaviruses; COVID-19; COVID-19 - diagnosis; COVID-19 - diagnostic imaging; COVID-19 - virology; Databases as Topic; Deep learning; Diagnosis; Health Sciences; Humans; Imaging, Three-Dimensional; Learning algorithms; Life Sciences; Logistic Models; Machine Learning; Mathematical and Computational Physics; Medical imaging; Medicine; Neural networks; Neural Networks, Computer; Original; Original Research Article; Pandemics; Principal components analysis; Regression analysis; SARS-CoV-2 - physiology; Statistics for Life Sciences; Theoretical; Theoretical and Computational Chemistry; Thorax - diagnostic imaging; Training; Viral diseases; Viruses; X-Rays; COVID-19; Artificial neural network; Computer-aided diagnosis; Image classification; Principal component analysis

Corona virus disease (COVID-19) acknowledged as a pandemic by the WHO and mankind all over the world is vulnerable to this virus. Alternative tools are needed that can help in diagnosis of the coronavirus. Researchers of this article investigated the potential of machine learning methods for automat...