{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T15:47:34Z","timestamp":1784216854424,"version":"3.55.0"},"reference-count":68,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T00:00:00Z","timestamp":1614124800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004054","name":"King Abdulaziz University","doi-asserted-by":"publisher","award":["GCV19-6-1441"],"award-info":[{"award-number":["GCV19-6-1441"]}],"id":[{"id":"10.13039\/501100004054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>The outbreak of coronavirus disease 2019 (COVID-19) has had an immense impact on world health and daily life in many countries. Sturdy observing of the initial site of infection in patients is crucial to gain control in the struggle with COVID-19. The early automated detection of the recent coronavirus disease (COVID-19) will help to limit its dissemination worldwide. Many initial studies have focused on the identification of the genetic material of coronavirus and have a poor detection rate for long-term surgery. The first imaging procedure that played an important role in COVID-19 treatment was the chest X-ray. Radiological imaging is often used as a method that emphasizes the performance of chest X-rays. Recent findings indicate the presence of COVID-19 in patients with irregular findings on chest X-rays. There are many reports on this topic that include machine learning strategies for the identification of COVID-19 using chest X-rays. Other current studies have used non-public datasets and complex artificial intelligence (AI) systems. In our research, we suggested a new COVID-19 identification technique based on the locality-weighted learning and self-organization map (LWL-SOM) strategy for detecting and capturing COVID-19 cases. We first grouped images from chest X-ray datasets based on their similar features in different clusters using the SOM strategy in order to discriminate between the COVID-19 and non-COVID-19 cases. Then, we built our intelligent learning model based on the LWL algorithm to diagnose and detect COVID-19 cases. The proposed SOM-LWL model improved the correlation coefficient performance results between the Covid19, no-finding, and pneumonia cases; pneumonia and no-finding cases; Covid19 and pneumonia cases; and Covid19 and no-finding cases from 0.9613 to 0.9788, 0.6113 to 1 0.8783 to 0.9999, and 0.8894 to 1, respectively. The proposed LWL-SOM had better results for discriminating COVID-19 and non-COVID-19 patients than the current machine learning-based solutions using AI evaluation measures.<\/jats:p>","DOI":"10.1371\/journal.pone.0247176","type":"journal-article","created":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T18:34:38Z","timestamp":1614191678000},"page":"e0247176","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":25,"title":["SOM-LWL method for identification of COVID-19 on chest X-rays"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8512-578X","authenticated-orcid":true,"given":"Ahmed Hamza","family":"Osman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3236-6504","authenticated-orcid":true,"given":"Hani Moetque","family":"Aljahdali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sultan Menwer","family":"Altarrazi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8944-8922","authenticated-orcid":true,"given":"Ali","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,2,24]]},"reference":[{"key":"pone.0247176.ref001","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1002\/jmv.25678","article-title":"Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle","volume":"vol. 92","author":"H. Lu","year":"2020","journal-title":"Journal of medical virology"},{"key":"pone.0247176.ref002","unstructured":"S. Minaee, R. Kafieh, M. Sonka, S. Yazdani, and G. J. Soufi, \u201cDeep-covid: Predicting covid-19 from chest x-ray images using deep transfer learning,\u201d arXiv preprint arXiv:2004.09363, 2020."},{"key":"pone.0247176.ref003","article-title":"Predicting the COVID-19 epidemic in Algeria using the SIR model","author":"M. S. Boudrioua","year":"2020","journal-title":"medRxiv"},{"key":"pone.0247176.ref004","unstructured":"A. Narin, C. Kaya, and Z. Pamuk, \u201cAutomatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks,\u201d arXiv preprint arXiv:2003.10849, 2020."},{"key":"pone.0247176.ref005","unstructured":"P. Groves, B. Kayyali, D. Knott, and S. V. Kuiken, \u201cThe\u2019big data\u2019revolution in healthcare: Accelerating value and innovation,\u201d 2016."},{"key":"pone.0247176.ref006","article-title":"Automated detection of COVID-19 cases using deep neural networks with X-ray images","author":"T. Ozturk","year":"2020","journal-title":"Computers in Biology and Medicine"},{"key":"pone.0247176.ref007","article-title":"Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study","author":"H. Shi","year":"2020","journal-title":"The Lancet Infectious Diseases"},{"key":"pone.0247176.ref008","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"vol. 3","author":"I. Guyon","year":"2003","journal-title":"Journal of machine learning research"},{"key":"pone.0247176.ref009","unstructured":"A. Janosi, W. Steinbrunn, M. Pfisterer, and R. Detrano, \u201cUCI machine learning repository-heart disease data set,\u201d School Inf. Comput. Sci., Univ. California, Irvine, CA, USA, 1988."},{"key":"pone.0247176.ref010","article-title":"Coronavirus disease 2019 (COVID-19): a perspective from China","author":"Z. Y. Zu","year":"2020","journal-title":"Radiology"},{"key":"pone.0247176.ref011","unstructured":"J. P. Kanne, B. P. Little, J. H. Chung, B. M. Elicker, and L. H. Ketai, \u201cEssentials for radiologists on COVID-19: an update\u2014radiology scientific expert panel,\u201d ed: Radiological Society of North America, 2020."},{"key":"pone.0247176.ref012","first-page":"200343","article-title":"Chest CT for typical 2019-nCoV pneumonia: relationship to negative RT-PCR testing","author":"X. Xie","year":"2020","journal-title":"Radiology"},{"key":"pone.0247176.ref013","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/S1473-3099(20)30134-1","article-title":"COVID-19 pneumonia: what has CT taught us?","volume":"vol. 20","author":"E. Y. Lee","year":"2020","journal-title":"The Lancet Infectious Diseases"},{"key":"pone.0247176.ref014","article-title":"Time course of lung changes on chest CT during recovery from 2019 novel coronavirus (COVID-19) pneumonia","author":"F. Pan","year":"2020","journal-title":"Radiology"},{"key":"pone.0247176.ref015","article-title":"Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection","author":"A. Bernheim","year":"2020","journal-title":"Radiology"},{"key":"pone.0247176.ref016","article-title":"Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT?","author":"C. Long","year":"2020","journal-title":"European journal of radiology"},{"key":"pone.0247176.ref017","first-page":"e200028","article-title":"Chest imaging appearance of COVID-19 infection","volume":"vol. 2","author":"W. Kong","year":"2020","journal-title":"Radiology: Cardiothoracic Imaging"},{"key":"pone.0247176.ref018","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.2214\/AJR.20.22954","article-title":"Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management","volume":"vol. 214","author":"Y. Li","year":"2020","journal-title":"American Journal of Roentgenology"},{"key":"pone.0247176.ref019","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/S0140-6736(20)30154-9","article-title":"A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster","volume":"vol. 395","author":"J. F.-W. Chan","year":"2020","journal-title":"The Lancet"},{"key":"pone.0247176.ref020","doi-asserted-by":"crossref","first-page":"494","DOI":"10.3348\/kjr.2020.0132","article-title":"Chest radiographic and CT findings of the 2019 novel coronavirus disease (COVID-19): analysis of nine patients treated in Korea","volume":"vol. 21","author":"S. H. Yoon","year":"2020","journal-title":"Korean journal of radiology"},{"key":"pone.0247176.ref021","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.2214\/AJR.20.22976","article-title":"Relation between chest CT findings and clinical conditions of coronavirus disease (COVID-19) pneumonia: a multicenter study","volume":"vol. 214","author":"W. Zhao","year":"2020","journal-title":"American Journal of Roentgenology"},{"key":"pone.0247176.ref022","article-title":"A survey on artificial intelligence approaches in supporting frontline workers and decision makers for COVID-19 pandemic","author":"J. Rasheed","year":"2020","journal-title":"Chaos, Solitons & Fractals"},{"key":"pone.0247176.ref023","unstructured":"E. Lorente, \u201cCOVID-19 pneumonia-evolution over a week,\u201d ed."},{"key":"pone.0247176.ref024","doi-asserted-by":"crossref","first-page":"9375","DOI":"10.1109\/ACCESS.2017.2788044","article-title":"Deep learning applications in medical image analysis","volume":"vol. 6","author":"J. Ker","year":"2017","journal-title":"Ieee Access"},{"key":"pone.0247176.ref025","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"vol. 19","author":"D. Shen","year":"2017","journal-title":"Annual review of biomedical engineering"},{"key":"pone.0247176.ref026","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cmpb.2018.04.005","article-title":"Deep learning for healthcare applications based on physiological signals: A review","volume":"vol. 161","author":"O. Faust","year":"2018","journal-title":"Computer methods and programs in biomedicine"},{"key":"pone.0247176.ref027","article-title":"Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review","author":"F. Murat","year":"2020","journal-title":"Computers in Biology and Medicine"},{"key":"pone.0247176.ref028","doi-asserted-by":"crossref","first-page":"39165","DOI":"10.1109\/ACCESS.2020.2976149","article-title":"An Effective of Ensemble Boosting Learning Method for Breast Cancer Virtual Screening Using Neural Network Model","volume":"vol. 8","author":"A. H. Osman","year":"2020","journal-title":"IEEE Access"},{"key":"pone.0247176.ref029","first-page":"1","article-title":"Efficient deep learning approach for augmented detection of Coronavirus disease","author":"A. Sedik","year":"2021","journal-title":"Neural Computing and Applications"},{"key":"pone.0247176.ref030","article-title":"An Efficient Method for Coronavirus Detection Through X-rays using deep Neural Network","author":"P. S. Rao","journal-title":"Current Medical Imaging"},{"key":"pone.0247176.ref031","first-page":"1","article-title":"Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images","author":"K. Kamal","year":"2021","journal-title":"Signal, Image and Video Processing"},{"key":"pone.0247176.ref032","first-page":"1","article-title":"COVIDScreen: Explainable deep learning framework for differential diagnosis of COVID-19 using chest X-Rays","author":"R. K. Singh","year":"2021","journal-title":"Neural Computing and Applications"},{"key":"pone.0247176.ref033","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.compbiomed.2018.09.009","article-title":"Arrhythmia detection using deep convolutional neural network with long duration ECG signals","volume":"vol. 102","author":"\u00d6. Y\u0131ld\u0131r\u0131m","year":"2018","journal-title":"Computers in biology and medicine"},{"key":"pone.0247176.ref034","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.compbiomed.2017.08.022","article-title":"A deep convolutional neural network model to classify heartbeats","volume":"vol. 89","author":"U. R. Acharya","year":"2017","journal-title":"Computers in biology and medicine"},{"key":"pone.0247176.ref035","first-page":"158","article-title":"An enhanced breast cancer diagnosis scheme based on two-step-SVM technique","volume":"vol. 8","author":"A. H. Osman","year":"2017","journal-title":"Int. J. Adv. Comput. Sci. Appl"},{"key":"pone.0247176.ref036","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2019.101673","article-title":"Convolutional neural networks for multi-class brain disease detection using MRI images","volume":"vol. 78","author":"M. Talo","year":"2019","journal-title":"Computerized Medical Imaging and Graphics"},{"key":"pone.0247176.ref037","unstructured":"P. Rajpurkar, J. Irvin, K. Zhu, B. Yang, H. Mehta, T. Duan, et al., \u201cRadiologist-level pneumonia detection on chest x-rays with deep learning,\u201d arXiv preprint arXiv:1711.05225 [cs, stat], 2017."},{"key":"pone.0247176.ref038","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.ins.2017.08.050","article-title":"Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network","volume":"vol. 420","author":"J. H. Tan","year":"2017","journal-title":"Information sciences"},{"key":"pone.0247176.ref039","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.cmpb.2019.06.005","article-title":"An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks","volume":"vol. 177","author":"J. C. Souza","year":"2019","journal-title":"Computer methods and programs in biomedicine"},{"key":"pone.0247176.ref040","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2020.108940","article-title":"Artificial intelligence in medical imaging: Game over for radiologists?","volume":"vol. 126","author":"F. Caobelli","year":"2020","journal-title":"European journal of radiology"},{"key":"pone.0247176.ref041","doi-asserted-by":"crossref","first-page":"769","DOI":"10.3390\/v12070769","article-title":"Deploying machine and deep learning models for efficient data-augmented detection of COVID-19 infections","volume":"vol. 12","author":"A. Sedik","year":"2020","journal-title":"Viruses"},{"key":"pone.0247176.ref042","unstructured":"E. E.-D. Hemdan, M. A. Shouman, and M. E. Karar, \u201cCovidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images,\u201d arXiv preprint arXiv:2003.11055, 2020."},{"key":"pone.0247176.ref043","unstructured":"L. a. W. Wang, A, \u201cCOVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images.,\u201d arXiv preprint arXiv:2003.09871, 2020."},{"key":"pone.0247176.ref044","first-page":"1","article-title":"Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks","author":"I. D. Apostolopoulos","year":"2020","journal-title":"Physical and Engineering Sciences in Medicine"},{"key":"pone.0247176.ref045","doi-asserted-by":"crossref","first-page":"418","DOI":"10.3390\/ai1030027","article-title":"A Deep Learning Approach to Detect COVID-19 Patients from Chest X-ray Images","volume":"vol. 1","author":"K. F. Haque","year":"2020","journal-title":"AI"},{"key":"pone.0247176.ref046","first-page":"2020","article-title":"Detection of coronavirus disease (covid-19) based on deep features","author":"P. K. Sethy","year":"2020","journal-title":"Preprints"},{"key":"pone.0247176.ref047","article-title":"Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images","author":"Y. Song","year":"2020","journal-title":"medRxiv"},{"key":"pone.0247176.ref048","doi-asserted-by":"crossref","first-page":"e0239474","DOI":"10.1371\/journal.pone.0239474","article-title":"A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity","volume":"vol. 15","author":"D. Goodman-Meza","year":"2020","journal-title":"PLoS One"},{"key":"pone.0247176.ref049","doi-asserted-by":"crossref","first-page":"e0241332","DOI":"10.1371\/journal.pone.0241332","article-title":"Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach","volume":"vol. 15","author":"C B.","year":"2020","journal-title":"PLoS ONE"},{"key":"pone.0247176.ref050","doi-asserted-by":"crossref","first-page":"e0235187","DOI":"10.1371\/journal.pone.0235187","article-title":"New machine learning method for image-based diagnosis of COVID-19","volume":"vol. 15","author":"M. A. Elaziz","year":"2020","journal-title":"Plos one"},{"key":"pone.0247176.ref051","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1137\/1109020","article-title":"On estimating regression","volume":"vol. 9","author":"E. A. Nadaraya","year":"1964","journal-title":"Theory of Probability & Its Applications"},{"key":"pone.0247176.ref052","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/978-94-017-2053-3_2","volume-title":"Lazy learning","author":"C. G. Atkeson","year":"1997"},{"key":"pone.0247176.ref053","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1214\/aos\/1176345969","article-title":"Optimal global rates of convergence for nonparametric regression","author":"C. J. Stone","year":"1982","journal-title":"The annals of statistics"},{"key":"pone.0247176.ref054","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/j.cemconcomp.2007.02.001","article-title":"Modeling slump flow of concrete using second-order regressions and artificial neural networks","volume":"vol. 29","author":"I.-C. Yeh","year":"2007","journal-title":"Cement and concrete composites"},{"key":"pone.0247176.ref055","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1214\/aos\/1176349022","article-title":"Local linear regression smoothers and their minimax efficiencies","author":"J. Fan","year":"1993","journal-title":"The annals of Statistics"},{"key":"pone.0247176.ref056","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1214\/ss\/1177011002","article-title":"Local regression: Automatic kernel carpentry","volume":"vol. 8","author":"T. Hastie","year":"1993","journal-title":"Statistical Science"},{"key":"pone.0247176.ref057","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.patcog.2017.05.025","article-title":"Handcrafted vs. non-handcrafted features for computer vision classification","volume":"vol. 71","author":"L. Nanni","year":"2017","journal-title":"Pattern Recognition"},{"key":"pone.0247176.ref058","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.asoc.2016.12.024","article-title":"An evaluation of convolutional neural networks for music classification using spectrograms","volume":"vol. 52","author":"Y. M. Costa","year":"2017","journal-title":"Applied soft computing"},{"key":"pone.0247176.ref059","unstructured":"B. Sebastian V, A. Unnikrishnan, and K. Balakrishnan, \u201cGray level co-occurrence matrices: generalisation and some new features,\u201d arXiv preprint arXiv:1205.4831, 2012."},{"key":"pone.0247176.ref060","volume-title":"Self-organizing Maps.-Springer Series in Information Sciences","author":"T. Kohonen","year":"2001"},{"key":"pone.0247176.ref061","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/72.846729","article-title":"Self organization of a massive document collection","volume":"vol. 11","author":"T. Kohonen","year":"2000","journal-title":"IEEE transactions on neural networks"},{"key":"pone.0247176.ref062","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1101\/gr.634603","article-title":"Informatics for unveiling hidden genome signatures","volume":"vol. 13","author":"T. Abe","year":"2003","journal-title":"Genome research"},{"key":"pone.0247176.ref063","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.neunet.2012.09.018","article-title":"Essentials of the self-organizing map","volume":"vol. 37","author":"T. Kohonen","year":"2013","journal-title":"Neural networks"},{"key":"pone.0247176.ref064","doi-asserted-by":"crossref","first-page":"2826","DOI":"10.1016\/j.microrel.2015.09.009","article-title":"Using SOM neural network for X-ray inspection of missing-bump defects in three-dimensional integration","volume":"vol. 55","author":"G. Liao","year":"2015","journal-title":"Microelectronics Reliability"},{"key":"pone.0247176.ref065","unstructured":"J. P. Cohen, P. Morrison, and L. Dao, \u201cCOVID-19 image data collection,\u201d arXiv preprint arXiv:2003.11597, 2020."},{"key":"pone.0247176.ref066","doi-asserted-by":"crossref","unstructured":"X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, \u201cChestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases,\u201d in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2097\u20132106.","DOI":"10.1109\/CVPR.2017.369"},{"key":"pone.0247176.ref067","article-title":"SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification","author":"E. Soares","year":"2020","journal-title":"medRxiv"},{"key":"pone.0247176.ref068","unstructured":"J. Zhao, Y. Zhang, X. He, and P. Xie, \u201cCOVID-CT-Dataset: a CT scan dataset about COVID-19,\u201d arXiv preprint arXiv:2003.13865, 2020."}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0247176","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T18:35:39Z","timestamp":1614191739000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0247176"}},"subtitle":[],"editor":[{"given":"Yuchen","family":"Qiu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,2,24]]},"references-count":68,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,2,24]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0247176","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,24]]}}}