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
• ISSN: 1913-2751; 1867-1462; 1867-1462
• DOI: 10.1007/s12539-020-00403-6

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 automatic diagnosis of corona virus with high accuracy from X-ray images. Two most commonly used classifiers were selected: logistic regression (LR) and convolutional neural networks (CNN). The main reason was to make the system fast and efficient. Moreover, a dimensionality reduction approach was also investigated based on principal component analysis (PCA) to further speed up the learning process and improve the classification accuracy by selecting the highly discriminate features. The deep learning-based methods demand large amount of training samples compared to conventional approaches, yet adequate amount of labelled training samples was not available for COVID-19 X-ray images. Therefore, data augmentation technique using generative adversarial network (GAN) was employed to further increase the training samples and reduce the overfitting problem. We used the online available dataset and incorporated GAN to have 500 X-ray images in total for this study. Both CNN and LR showed encouraging results for COVID-19 patient identification. The LR and CNN models showed 95.2–97.6% overall accuracy without PCA and 97.6–100% with PCA for positive cases identification, respectively.