Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infectio
- PDF / 1,695,642 Bytes
- 18 Pages / 595.276 x 790.866 pts Page_size
- 91 Downloads / 169 Views
RESEARCH
BioMedical Engineering OnLine Open Access
Machine‑learning classification of texture features of portable chest X‑ray accurately classifies COVID‑19 lung infection Lal Hussain1,2* , Tony Nguyen3, Haifang Li3, Adeel A. Abbasi1, Kashif J. Lone1, Zirun Zhao3, Mahnoor Zaib2, Anne Chen3 and Tim Q. Duong3 *Correspondence: [email protected] 1 Department of Computer Science and IT, King Abdullah Campus, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Azad Kashmir, Pakistan Full list of author information is available at the end of the article
Abstract Background: The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. Purpose: The study aimed at developing an AI imaging analysis tool to classify COVID19 lung infection based on portable CXRs. Materials and methods: Public datasets of COVID-19 (N = 130), bacterial pneumonia (N = 145), non-COVID-19 viral pneumonia (N = 145), and normal (N = 138) CXRs were analyzed. Texture and morphological features were extracted. Five supervised machine-learning AI algorithms were used to classify COVID-19 from other conditions. Two-class and multi-class classification were performed. Statistical analysis was done using unpaired two-tailed t tests with unequal variance between groups. Performance of classification models used the receiver-operating characteristic (ROC) curve analysis. Results: For the two-class classification, the accuracy, sensitivity and specificity were, respectively, 100%, 100%, and 100% for COVID-19 vs normal; 96.34%, 95.35% and 97.44% for COVID-19 vs bacterial pneumonia; and 97.56%, 97.44% and 97.67% for COVID-19 vs non-COVID-19 viral pneumonia. For the multi-class classification, the combined accuracy and AUC were 79.52% and 0.87, respectively. Conclusion: AI classification of texture and morphological features of portable CXRs accurately distinguishes COVID-19 lung infection in patients in multi-class datasets. Deep-learning methods have the potential to improve diagnostic efficiency and accuracy for portable CXRs. Keywords: Texture, Morphological, Machine learning, Feature extraction, Classification, COVID-19
Background In December 2019, in the Wuhan Hubei province of China, a cluster of cases of pneumonia with an unknown cause was reported [1]. Eventually, it was discovered as severe © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons l
Data Loading...
