{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T17:25:16Z","timestamp":1788629116689,"version":"build-2803163510"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,4,30]],"date-time":"2022-04-30T00:00:00Z","timestamp":1651276800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1016\/j.eswa.2022.117360","type":"journal-article","created":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T02:15:41Z","timestamp":1651457741000},"page":"117360","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":24,"special_numbering":"C","title":["DMDF-Net: Dual multiscale dilated fusion network for accurate segmentation of lesions related to COVID-19 in lung radiographic scans"],"prefix":"10.1016","volume":"202","author":[{"given":"Muhammad","family":"Owais","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na Rae","family":"Baek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang Ryoung","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2022.117360_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106647","article-title":"FSS-2019-nCov: A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection","volume":"212","author":"Abdel-Basset","year":"2021","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2022.117360_b0010","doi-asserted-by":"crossref","first-page":"E32","DOI":"10.1148\/radiol.2020200642","article-title":"Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases","volume":"296","author":"Ai","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.eswa.2022.117360_b0015","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.eswa.2022.117360_b0020","series-title":"In Proceedings of the European Conference on Computer Vision","first-page":"801","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"Chen","year":"2018"},{"key":"10.1016\/j.eswa.2022.117360_b0025","series-title":"In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"248","article-title":"ImageNet: A large-scale hierarchical image database","author":"Deng","year":"2009"},{"key":"10.1016\/j.eswa.2022.117360_b0030","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.303","article-title":"A multi-task pipeline with specialized streams for classification and segmentation of infection manifestations in covid-19 scans","volume":"6","author":"El-Bana","year":"2020","journal-title":"PeerJ Computer Science"},{"key":"10.1016\/j.eswa.2022.117360_b0035","series-title":"In Proceedings of the 27th International Joint Conference on Artificial Intelligence","first-page":"698","article-title":"Enhanced-alignment measure for binary foreground map evaluation","author":"Fan","year":"2018"},{"key":"10.1016\/j.eswa.2022.117360_b0040","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1109\/TMI.2020.2996645","article-title":"Inf-net: Automatic covid-19 lung infection segmentation from CT images","volume":"39","author":"Fan","year":"2020","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.eswa.2022.117360_b0045","article-title":"Cognitive vision inspired object segmentation metric and loss function","volume":"6","author":"Fan","year":"2021","journal-title":"SCIENTIA SINICA Informationis"},{"key":"10.1016\/j.eswa.2022.117360_b0050","doi-asserted-by":"crossref","first-page":"E115","DOI":"10.1148\/radiol.2020200432","article-title":"Sensitivity of chest CT for COVID-19: Comparison to RT-PCR","volume":"296","author":"Fang","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.eswa.2022.117360_b0055","unstructured":"Heaton, J., 2015. Artificial Intelligence for Humans, Deep learning and neural networks. Heaton Research Inc.: St. Louis, MO, USA, Volume 3."},{"key":"10.1016\/j.eswa.2022.117360_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102092","article-title":"Early neoplasia identification in Barrett\u2019s esophagus via attentive hierarchical aggregation and self-distillation","volume":"72","author":"Hou","year":"2021","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.eswa.2022.117360_b0065","doi-asserted-by":"crossref","unstructured":"Jadon, S., 2020. A survey of loss functions for semantic segmentation. In Proceedings of the IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, Via del Mar, Chile, 27-29 October, pp. 1\u20137.","DOI":"10.1109\/CIBCB48159.2020.9277638"},{"key":"10.1016\/j.eswa.2022.117360_b0070","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1109\/JBHI.2020.3042523","article-title":"COVID-19 CT image synthesis with a conditional generative adversarial network","volume":"25","author":"Jiang","year":"2021","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"10.1016\/j.eswa.2022.117360_b0075","first-page":"315","article-title":"Accelerating stochastic gradient descent using predictive variance reduction","volume":"26","author":"Johnson","year":"2013","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2022.117360_b0080","unstructured":"Jun, M., Cheng, G., Yixin, W., Xingle, A., Jiantao, G., Ziqi, Y., et al., 2021. COVID-19 CT lung and infection segmentation dataset (Version 1.0). Zenodo. http:\/\/doi.org\/10.5281\/zenodo.3757476."},{"key":"10.1016\/j.eswa.2022.117360_b0085","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.icte.2020.04.010","article-title":"The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset","volume":"6","author":"Kandel","year":"2020","journal-title":"ICT Express"},{"key":"10.1016\/j.eswa.2022.117360_b0090","series-title":"In Proceedings of International Conference on System, Computation, Automation and Networking (ICSCAN)","first-page":"1","article-title":"Res-UNet supported segmentation and evaluation of COVID19 lesion in lung CT","author":"Kesavan","year":"2021"},{"key":"10.1016\/j.eswa.2022.117360_b0095","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1038\/s41591-021-01230-y","article-title":"Looking beyond COVID-19 vaccine phase 3 trials","volume":"27","author":"Kim","year":"2021","journal-title":"Nature Medicine"},{"key":"10.1016\/j.eswa.2022.117360_b0100","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2022.117360_b0105","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1109\/TNNLS.2020.2995319","article-title":"Transformation-consistent self-ensembling model for semisupervised medical image segmentation","volume":"32","author":"Li","year":"2021","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.eswa.2022.117360_b0110","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1109\/TNNLS.2017.2672978","article-title":"Preconditioned stochastic gradient descent","volume":"29","author":"Li","year":"2018","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.eswa.2022.117360_b0115","doi-asserted-by":"crossref","first-page":"2787","DOI":"10.1109\/JBHI.2020.3018181","article-title":"Efficient and effective training of COVID-19 classification networks with self-supervised dual-track learning to rank","volume":"24","author":"Li","year":"2020","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"10.1016\/j.eswa.2022.117360_b0120","series-title":"In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015"},{"key":"10.1016\/j.eswa.2022.117360_b0125","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1002\/mp.14676","article-title":"Towards data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation","volume":"48","author":"Ma","year":"2021","journal-title":"Medical Physics"},{"key":"10.1016\/j.eswa.2022.117360_b0130","doi-asserted-by":"crossref","unstructured":"Margolin, R., Zelnik-Manor, L., Tal, A., 2014. How to evaluate foreground maps?. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA, 23\u201328 June, pp. 248\u2013255.","DOI":"10.1109\/CVPR.2014.39"},{"key":"10.1016\/j.eswa.2022.117360_b0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101768","article-title":"Brain graph super-resolution for boosting neurological disorder diagnosis using unsupervised multi-topology connectional brain template learning","volume":"65","author":"Mhiri","year":"2020","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.eswa.2022.117360_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101794","article-title":"Deep-covid: Predicting covid-19 from chest x-ray images using deep transfer learning","volume":"65","author":"Minaee","year":"2020","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.eswa.2022.117360_b0145","doi-asserted-by":"crossref","unstructured":"Morozov, S.P., Andreychenko, A.E., Pavlov, N.A., Vladzymyrskyy, A.V., Ledikhova, N.V., Gombolevskiy, V.A., et al., 2020. Mosmeddata: Chest CT scans with covid-19 related findings dataset. arXiv preprint arXiv:2005.06465.","DOI":"10.1101\/2020.05.20.20100362"},{"key":"10.1016\/j.eswa.2022.117360_b0150","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1016\/j.bbe.2021.06.011","article-title":"FractalCovNet architecture for COVID-19 Chest X-ray image classification and CT-scan image segmentation","volume":"41","author":"Munusamy","year":"2021","journal-title":"Biocybernetics and Biomedical Engineering"},{"key":"10.1016\/j.eswa.2022.117360_b0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2019.105121","article-title":"GC-Net: Global context network for medical image segmentation","volume":"190","author":"Ni","year":"2020","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"10.1016\/j.eswa.2022.117360_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107747","article-title":"Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images","volume":"114","author":"Oulefki","year":"2021","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.eswa.2022.117360_b0165","doi-asserted-by":"crossref","first-page":"1008","DOI":"10.3390\/jpm11101008","article-title":"Domain-adaptive artificial intelligence-based model for personalized diagnosis of trivial lesions related to COVID-19 in chest computed tomography scans","volume":"11","author":"Owais","year":"2021","journal-title":"Journal of Personalized Medicine"},{"key":"10.1016\/j.eswa.2022.117360_b0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.107490","article-title":"Light-weighted ensemble network with multilevel activation visualization for robust diagnosis of COVID19 pneumonia from large-scale chest radiographic database","volume":"108","author":"Owais","year":"2021","journal-title":"Applied Soft Computing"},{"key":"10.1016\/j.eswa.2022.117360_b0175","series-title":"In Proceedings of the International Conference on Science and Technology-Computer","first-page":"1","article-title":"Duplicate question detection in question answer website using convolutional neural network","author":"Prabowo","year":"2019"},{"key":"10.1016\/j.eswa.2022.117360_b0180","doi-asserted-by":"crossref","unstructured":"Rathod, S.R., Khanuja, H.K., 2021. Automatic segmentation of COVID-19 pneumonia lesions and its classification from CT images: A survey. In 2021 International Conference on Intelligent Technologies (CONIT), Hubli, India, 25\u201327 June, pp. 1\u20138.","DOI":"10.1109\/CONIT51480.2021.9498350"},{"key":"10.1016\/j.eswa.2022.117360_b0185","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/38.946629","article-title":"Color transfer between images","volume":"21","author":"Reinhard","year":"2001","journal-title":"IEEE Computer Graphics and Applications"},{"key":"10.1016\/j.eswa.2022.117360_b0190","series-title":"In International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.eswa.2022.117360_b0195","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.compmedimag.2018.03.001","article-title":"An application of cascaded 3D fully convolutional networks for medical image segmentation","volume":"66","author":"Roth","year":"2018","journal-title":"Computerized Medical Imaging and Graphics"},{"key":"10.1016\/j.eswa.2022.117360_b0200","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C., 2018. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18-23 June, pp. 4510\u20134520.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"10.1016\/j.eswa.2022.117360_b0205","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1002\/ima.22525","article-title":"An integrated feature frame work for automated segmentation of COVID-19 infection from lung CT images","volume":"31","author":"Selvaraj","year":"2021","journal-title":"International Journal of Imaging Systems and Technology"},{"key":"10.1016\/j.eswa.2022.117360_b0210","series-title":"In Proceedings of the 14th PErvasive Technologies Related to Assistive Environments Conference","first-page":"404","article-title":"Deep learning models for COVID-19 infected area segmentation in CT images","author":"Voulodimos","year":"2021"},{"key":"10.1016\/j.eswa.2022.117360_b0215","unstructured":"World Health Organization, WHO Coronavirus Disease (COVID-19) Dashboard. Available online: https:\/\/covid19.who.int\/ (accessed on 18 June 2021)."},{"key":"10.1016\/j.eswa.2022.117360_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101772","article-title":"MSCS-DeepLN: Evaluating lung nodule malignancy using multi-scale cost-sensitive neural networks","volume":"65","author":"Xu","year":"2020","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.eswa.2022.117360_b0225","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1186\/s12931-020-01440-x","article-title":"Relationship of chest CT score with clinical characteristics of 108 patients hospitalized with COVID-19 in Wuhan, China","volume":"21","author":"Zhang","year":"2020","journal-title":"Respiratory Research"},{"key":"10.1016\/j.eswa.2022.117360_b0230","doi-asserted-by":"crossref","first-page":"901","DOI":"10.3390\/diagnostics10110901","article-title":"CoSinGAN: Learning COVID-19 infection segmentation from a single radiological image","volume":"10","author":"Zhang","year":"2020","journal-title":"Diagnostics"},{"key":"10.1016\/j.eswa.2022.117360_b0235","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104526","article-title":"D2A U-Net: Automatic Segmentation of COVID-19 CT Slices Based on Dual Attention and Hybrid Dilated Convolution","volume":"135","author":"Zhao","year":"2021","journal-title":"Computers in Biology and Medicine"},{"key":"10.1016\/j.eswa.2022.117360_b0240","doi-asserted-by":"crossref","first-page":"5637","DOI":"10.1007\/s10462-020-09830-9","article-title":"Image segmentation evaluation: A survey of methods","volume":"53","author":"Zhaobin","year":"2020","journal-title":"Artificial Intelligence Review"},{"key":"10.1016\/j.eswa.2022.117360_b0245","doi-asserted-by":"crossref","first-page":"185786","DOI":"10.1109\/ACCESS.2020.3027738","article-title":"MSD-Net: Multi-scale discriminative network for COVID-19 lung infection segmentation on CT","volume":"8","author":"Zheng","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.eswa.2022.117360_b0250","doi-asserted-by":"crossref","first-page":"9999368","DOI":"10.1155\/2021\/9999368","article-title":"Improved 3D U-Net for COVID-19 chest CT image segmentation","volume":"2021","author":"Zheng","year":"2021","journal-title":"Scientific Programming"},{"key":"10.1016\/j.eswa.2022.117360_b0255","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A., 2016. Learning deep features for discriminative localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27-30 June, pp. 2921\u20132929.","DOI":"10.1109\/CVPR.2016.319"},{"key":"10.1016\/j.eswa.2022.117360_b0260","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1002\/ima.22527","article-title":"Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism","volume":"31","author":"Zhou","year":"2021","journal-title":"International Journal of Imaging Systems and Technology"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417422007126?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417422007126?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T05:48:00Z","timestamp":1760680080000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417422007126"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9]]},"references-count":52,"alternative-id":["S0957417422007126"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2022.117360","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2022,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DMDF-Net: Dual multiscale dilated fusion network for accurate segmentation of lesions related to COVID-19 in lung radiographic scans","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2022.117360","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"117360"}}