{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T01:17:06Z","timestamp":1773019026458,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:00:00Z","timestamp":1667779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61762085"],"award-info":[{"award-number":["61762085"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019D01C081"],"award-info":[{"award-number":["2019D01C081"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Xinjiang Uygur Autonomous Region","award":["61762085"],"award-info":[{"award-number":["61762085"]}]},{"name":"Natural Science Foundation of Xinjiang Uygur Autonomous Region","award":["2019D01C081"],"award-info":[{"award-number":["2019D01C081"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, the COVID-19 pandemic coronavirus has put a lot of pressure on health systems around the world. One of the most common ways to detect COVID-19 is to use chest X-ray images, which have the advantage of being cheap and fast. However, in the early days of the COVID-19 outbreak, most studies applied pretrained convolutional neural network (CNN) models, and the features produced by the last convolutional layer were directly passed into the classification head. In this study, the proposed ensemble model consists of three lightweight networks, Xception, MobileNetV2 and NasNetMobile as three original feature extractors, and then three base classifiers are obtained by adding the coordinated attention module, LSTM and a new classification head to the original feature extractors. The classification results from the three base classifiers are then fused by a confidence fusion method. Three publicly available chest X-ray datasets for COVID-19 testing were considered, with ternary (COVID-19, normal and other pneumonia) and quaternary (COVID-19, normal) analyses performed on the first two datasets, bacterial pneumonia and viral pneumonia classification, and achieved high accuracy rates of 95.56% and 91.20%, respectively. The third dataset was used to compare the performance of the model compared to other models and the generalization ability on different datasets. We performed a thorough ablation study on the first dataset to understand the impact of each proposed component. Finally, we also performed visualizations. These saliency maps not only explain key prediction decisions of the model, but also help radiologists locate areas of infection. Through extensive experiments, it was finally found that the results obtained by the proposed method are comparable to the state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/s22218578","type":"journal-article","created":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T08:17:12Z","timestamp":1667895432000},"page":"8578","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["COVIDX-LwNet: A Lightweight Network Ensemble Model for the Detection of COVID-19 Based on Chest X-ray Images"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8478-0483","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830017, China"}]},{"given":"Shuxian","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830017, China"}]},{"given":"Huan","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830017, China"}]},{"given":"Le","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830017, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,7]]},"reference":[{"key":"ref_1","unstructured":"(2022, October 19). Available online: https:\/\/www.who.int\/emergencies\/diseases\/novel-coronavirus-2019\/interactive-timeline#!."},{"key":"ref_2","unstructured":"(2022, October 19). Available online: https:\/\/www.who.int\/publications\/m\/item\/weekly-epidemiological-update-on-covid-19---19-october-2022."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"459","DOI":"10.5694\/mja2.50569","article-title":"Isolation and rapid sharing of the 2019 novel coronavirus (SARS-CoV-2) from the first patient diagnosed with COVID-19 in Australia","volume":"212","author":"Caly","year":"2020","journal-title":"Med. J. Aust."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s40779-020-0233-6","article-title":"A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-ncov) infected pneumonia (standard version)","volume":"7","author":"Jin","year":"2020","journal-title":"Military Med. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/s41479-016-0012-z","article-title":"The definition and classification of pneumonia","volume":"8","author":"Mackenzie","year":"2016","journal-title":"Pneumonia"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103326","DOI":"10.1016\/j.bspc.2021.103326","article-title":"A novel data augmentation based on Gabor filter and convolutional deep learning for improving the classification of COVID-19 chest X-ray images","volume":"72","author":"Barshooi","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"200463","DOI":"10.1148\/radiol.2020200463","article-title":"Chest CT findings in coronavirus disease-19 (COVID-19): Relationship to duration of infection","volume":"295","author":"Bernheim","year":"2020","journal-title":"Radiology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1007\/s00530-021-00826-1","article-title":"Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images","volume":"28","author":"Ravi","year":"2022","journal-title":"Multimed. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105350","DOI":"10.1016\/j.compbiomed.2022.105350","article-title":"COVID-19 image classification using deep learning: Advances, challenges and opportunities","volume":"144","author":"Aggarwal","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1038\/s41551-021-00704-1","article-title":"A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images","volume":"5","author":"Wang","year":"2021","journal-title":"Nat. Biomed. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Afifi, A., Hafsa, N.E., Ali, M.A.S., Alhumam, A., and Alsalman, S. (2021). An ensemble of global and local-attention based convolutional neural networks for COVID-19 diagnosis on chest X-ray images. Symmetry, 13.","DOI":"10.3390\/sym13010113"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00530-021-00857-8","article-title":"A light-weight convolutional Neural Network Architecture for classification of COVID-19 chest X-ray images","volume":"28","author":"Masud","year":"2022","journal-title":"Multimed. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1109\/JBHI.2022.3151171","article-title":"COVID detection from Chest X-ray Images using multi-scale attention","volume":"26","author":"Dhere","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42979-021-00881-5","article-title":"A transfer learning-based approach with deep cnn for covid-19-and pneumonia-affected chest x-ray image classification","volume":"3","author":"Chakraborty","year":"2022","journal-title":"SN Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Khan, E., Rehman, M.Z.U., Ahmed, F., Alfouzan, F.A., Alzahrani, N.M., and Ahmad, J. (2022). Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques. Sensors, 22.","DOI":"10.3390\/s22031211"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103272","DOI":"10.1016\/j.bspc.2021.103272","article-title":"CovXmlc: High performance COVID-19 detection on X-ray images using Multi-Model classification","volume":"71","author":"Verma","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5407","DOI":"10.1007\/s11042-021-11787-y","article-title":"Adaptive UNet-based Lung Segmentation and Ensemble Learning with CNN-based Deep Features for Automated COVID-19 Diagnosis","volume":"81","author":"Das","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103778","DOI":"10.1016\/j.bspc.2022.103778","article-title":"A Novel Fusion based Convolutional Neural Network Approach for Classification of COVID-19 from Chest X-ray Images","volume":"77","author":"Sharma","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"103677","DOI":"10.1016\/j.bspc.2022.103677","article-title":"Deep feature fusion classification network (DFFCNet): Towards accurate diagnosis of COVID-19 using chest X-rays images","volume":"76","author":"Liu","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Khan, S.H., Sohail, A., Khan, A., and Lee, Y.-S. (2022). COVID-19 detection in chest X-ray images using a new channel boosted CNN. Diagnostics, 12.","DOI":"10.3390\/diagnostics12020267"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.ins.2022.01.062","article-title":"Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis","volume":"592","author":"Mahbub","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105581","DOI":"10.1016\/j.cmpb.2020.105581","article-title":"CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest X-ray images","volume":"196","author":"Khan","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tartaglione, E., Barbano, C.A., Berzovini, C., Calandri, M., and Grangetto, M. (2020). Unveiling COVID-19 from chest X-ray with deep learning: A hurdles race with small data. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17186933"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., and Le, Q.V. (2018, January 18\u201323). Learning transferable architectures for scalable image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021). Coordinate Attention for Efficient Mobile Network Design. arXiv.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: A search space odyssey","volume":"28","author":"Greff","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_29","first-page":"66496","article-title":"Fusion methods for CNN-Based automatic modulation classification","volume":"7","author":"Zheng","year":"2019","journal-title":"IEEE Access Spec. Sect. Artif. Intell. Phys.-Layer Wirel. Commun."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chakraborty, S., Murali, B., and Mitra, A.K. (2022). An efficient deep learning model to detect COVID-19 using chest X-ray images. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19042013"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"19549","DOI":"10.1038\/s41598-020-76550-z","article-title":"Covid-net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images","volume":"10","author":"Wang","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"100405","DOI":"10.1016\/j.imu.2020.100405","article-title":"COVID faster R-CNN: A novel framework to diagnose novel coronavirus disease (COVID-19) in X-ray images","volume":"20","author":"Shibly","year":"2020","journal-title":"Inform. Med. Unlocked."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"103792","DOI":"10.1016\/j.compbiomed.2020.103792","article-title":"Automated detection of COVID-19 cases using deep neural networks with X-ray images","volume":"121","author":"Ozturk","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Law, B.K., and Lin, L.P. (2021, January 13\u201315). Development of A Deep Learning Model to Classify X-ray of Covid-19, Normal and Pneumonia-Affected Patients. Proceedings of the IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Virtual.","DOI":"10.1109\/ICSIPA52582.2021.9576804"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"102583","DOI":"10.1016\/j.bspc.2021.102583","article-title":"Diagnosing Covid-19 chest X-rays with a lightweight truncated DenseNet with partial layer freezing and feature fusion","volume":"68","author":"Montalbo","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103182","DOI":"10.1016\/j.bspc.2021.103182","article-title":"A deep learning based approach for automatic detection of COVID-19 cases using chest X-ray images","volume":"71","author":"Bhattacharyya","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Yosinski, J., and Clune, J. (2015, January 7\u201312). Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2688","DOI":"10.1109\/TMI.2020.2993291","article-title":"Deep learning COVID-19 features on CXR using limited training data sets","volume":"39","author":"Oh","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1109\/TNNLS.2021.3070467","article-title":"Convolutional sparse support estimator-based COVID-19 recognition from X-ray images","volume":"32","author":"Yamac","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"108867","DOI":"10.1016\/j.asoc.2022.108867","article-title":"COVID-19 prognosis using limited chest X-ray images","volume":"122","author":"Mondal","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_42","unstructured":"Gal, Y., and Ghahramani, Z. (2016, January 20\u201322). Dropout as a bayesian approximation: Representing model uncertainty in deep learning. Proceedings of the International Conference on Machine Learning (PMLR), New York, NY, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8578\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:12:09Z","timestamp":1760145129000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8578"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,7]]},"references-count":42,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22218578"],"URL":"https:\/\/doi.org\/10.3390\/s22218578","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,7]]}}}