{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:35:51Z","timestamp":1775838951480,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T00:00:00Z","timestamp":1720396800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T00:00:00Z","timestamp":1720396800000},"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":["62071381"],"award-info":[{"award-number":["62071381"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015401","name":"Key Research and Development Projects of Shaanxi Province","doi-asserted-by":"publisher","award":["2024GX-YBXM-149"],"award-info":[{"award-number":["2024GX-YBXM-149"]}],"id":[{"id":"10.13039\/501100015401","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis. Comput. Ind. Biomed. Art"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Pneumonia is a serious disease that can be fatal, particularly among children and the elderly. The accuracy of pneumonia diagnosis can be improved by combining artificial-intelligence technology with X-ray imaging. This study proposes X-ODFCANet, which addresses the issues of low accuracy and excessive parameters in existing deep-learning-based pneumonia-classification methods. This network incorporates a feature coordination attention module and an omni-dimensional dynamic convolution (ODConv) module, leveraging the residual module for feature extraction from X-ray images. The feature coordination attention module utilizes two one-dimensional feature encoding processes to aggregate feature information from different spatial directions. Additionally, the ODConv module extracts and fuses feature information in four dimensions: the spatial dimension of the convolution kernel, input and output channel quantities, and convolution kernel quantity. The experimental results demonstrate that the proposed method can effectively improve the accuracy of pneumonia classification, which is 3.77% higher than that of ResNet18. The model parameters are 4.45M, which was reduced by approximately 2.5 times. The code is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/limuni\/X-ODFCANET\">https:\/\/github.com\/limuni\/X-ODFCANET<\/jats:ext-link>.<\/jats:p>","DOI":"10.1186\/s42492-024-00168-5","type":"journal-article","created":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T04:01:52Z","timestamp":1720411312000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Omni-dimensional dynamic convolution feature coordinate attention network for pneumonia classification"],"prefix":"10.1186","volume":"7","author":[{"given":"Yufei","family":"Li","sequence":"first","affiliation":[]},{"given":"Yufei","family":"Xin","sequence":"additional","affiliation":[]},{"given":"Xinni","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yinrui","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Cheng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Zhengwen","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Shaoyi","family":"Du","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1026-0060","authenticated-orcid":false,"given":"Lin","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,8]]},"reference":[{"issue":"10258","key":"168_CR1","first-page":"1204","volume":"396","author":"GBD","year":"2019","unstructured":"GBD 2019 Diseases and Injuries Collaborators (2020) Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the global burden of disease study 2019. Lancet 396(10258):1204\u20131222","journal-title":"Lancet"},{"key":"168_CR2","doi-asserted-by":"publisher","unstructured":"Fadel SA, Boschi-Pinto C, Yu SC, Reynales-Shigematsu LM, Menon GR, Newcombe L et al (2019) Trends in cause-specific mortality among children aged 5-14 years from 2005 to 2016 in India, China, Brazil, and Mexico: an analysis of nationally representative mortality studies. Lancet 393(10176):1119\u20131127. https:\/\/doi.org\/10.1016\/S0140-6736(19)30220-X","DOI":"10.1016\/S0140-6736(19)30220-X"},{"key":"168_CR3","doi-asserted-by":"publisher","unstructured":"Baek MS, Park S, Choi JH, Kim CH, Hyun IG (2020) Mortality and prognostic prediction in very elderly patients with severe pneumonia. J Intensive Care Med 35(12):1405\u20131410. https:\/\/doi.org\/10.1177\/0885066619826045","DOI":"10.1177\/0885066619826045"},{"key":"168_CR4","doi-asserted-by":"publisher","first-page":"105123","DOI":"10.1016\/j.compbiomed.2021.105123","volume":"141","author":"H Hassan","year":"2022","unstructured":"Hassan H, Ren ZY, Zhao HS, Huang SJ, Li D, Xiang SH et al (2022) Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks. Comput Biol Med 141:105123. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.105123","journal-title":"Comput Biol Med"},{"issue":"4","key":"168_CR5","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/j.jinf.2020.02.018","volume":"80","author":"SJ Tian","year":"2020","unstructured":"Tian SJ, Hu N, Lou J, Chen K, Kang XQ, Xiang ZJ et al (2020) Characteristics of COVID-19 infection in Beijing. J Infect 80(4):401\u2013406. https:\/\/doi.org\/10.1016\/j.jinf.2020.02.018","journal-title":"J Infect"},{"key":"168_CR6","doi-asserted-by":"publisher","first-page":"102571","DOI":"10.1016\/j.artmed.2023.102571","volume":"142","author":"MHT Najaran","year":"2023","unstructured":"Najaran MHT (2023) A genetic programming-based convolutional deep learning algorithm for identifying COVID-19 cases via X-ray images. Artif Intell Med 142:102571. https:\/\/doi.org\/10.1016\/j.artmed.2023.102571","journal-title":"Artif Intell Med"},{"key":"168_CR7","doi-asserted-by":"publisher","first-page":"109906","DOI":"10.1016\/j.asoc.2022.109906","volume":"133","author":"G Celik","year":"2023","unstructured":"Celik G (2023) Detection of covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network. Appl Soft Comput 133:109906. https:\/\/doi.org\/10.1016\/j.asoc.2022.109906","journal-title":"Appl Soft Comput"},{"key":"168_CR8","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.clinimag.2021.05.027","volume":"79","author":"S Yucel","year":"2021","unstructured":"Yucel S, Aycicek T, Bilgici MC, Dincer OS, Tomak L (2021) 3 tesla MRI in diagnosis and follow up of children with pneumonia. Clin Imaging 79:213\u2013218. https:\/\/doi.org\/10.1016\/j.clinimag.2021.05.027","journal-title":"Clin Imaging"},{"issue":"1","key":"168_CR9","first-page":"4","volume":"7","author":"YH Jin","year":"2020","unstructured":"Jin YH, Cai L, Cheng ZS, Cheng H, Deng T, Fan YP et al (2020) A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version). Mil Med Res 7(1):4","journal-title":"Mil Med Res"},{"issue":"2","key":"168_CR10","doi-asserted-by":"publisher","first-page":"E32","DOI":"10.1148\/radiol.2020200642","volume":"296","author":"T Ai","year":"2020","unstructured":"Ai T, Yang ZL, Hou HY, Zhan CN, Chen C, Lv WZ et al (2020) Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 296(2):E32\u2013E40. https:\/\/doi.org\/10.1148\/radiol.2020200642","journal-title":"Radiology"},{"issue":"5","key":"168_CR11","doi-asserted-by":"publisher","first-page":"2819","DOI":"10.1007\/s00330-020-07347-x","volume":"31","author":"A Kov\u00e1cs","year":"2021","unstructured":"Kov\u00e1cs A, Pal\u00e1sti P, Ver\u00e9b D, Bozsik B, Palk\u00f3 A, Kincses ZT (2021) The sensitivity and specificity of chest CT in the diagnosis of COVID-19. Eur Radiol 31(5):2819\u20132824. https:\/\/doi.org\/10.1007\/s00330-020-07347-x","journal-title":"Eur Radiol"},{"issue":"2","key":"168_CR12","doi-asserted-by":"publisher","first-page":"E65","DOI":"10.1148\/radiol.2020200905","volume":"296","author":"L Li","year":"2020","unstructured":"Li L, Qin LX, Xu ZG, Yin YB, Wang X, Kong B et al (2020) Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy. Radiology 296(2):E65\u2013E71. https:\/\/doi.org\/10.1148\/radiol.2020200905","journal-title":"Radiology"},{"key":"168_CR13","doi-asserted-by":"publisher","first-page":"105350","DOI":"10.1016\/j.compbiomed.2022.105350","volume":"144","author":"P Aggarwal","year":"2022","unstructured":"Aggarwal P, Mishra NK, Fatimah B, Singh P, Gupta A, Joshi SD (2022) COVID-19 image classification using deep learning: Advances, challenges and opportunities. Comput Biol Med 144:105350. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105350","journal-title":"Comput Biol Med"},{"issue":"1","key":"168_CR14","doi-asserted-by":"publisher","first-page":"16071","DOI":"10.1038\/s41598-021-95680-6","volume":"11","author":"J Hou","year":"2021","unstructured":"Hou J, Gao T (2021) Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection. Sci Rep 11(1):16071. https:\/\/doi.org\/10.1038\/s41598-021-95680-6","journal-title":"Sci Rep"},{"key":"168_CR15","doi-asserted-by":"publisher","first-page":"109236","DOI":"10.1016\/j.ejrad.2020.109236","volume":"131","author":"CO Serrano","year":"2020","unstructured":"Serrano CO, Alonso E, Andr\u00e9s M, Buitrago N, Vigara AP, Pajares MP et al (2020) Pediatric chest X-ray in COVID-19 infection. Eur J Radiol 131:109236. https:\/\/doi.org\/10.1016\/j.ejrad.2020.109236","journal-title":"Eur J Radiol"},{"issue":"1","key":"168_CR16","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1148\/radiol.2020201365","volume":"296","author":"GD Rubin","year":"2020","unstructured":"Rubin GD, Ryerson CJ, Haramati LB, Sverzellati N, Kanne JP, Raoof S et al (2020) The role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the fleischner society. Radiology 296(1):172\u2013180. https:\/\/doi.org\/10.1148\/radiol.2020201365","journal-title":"Radiology"},{"key":"168_CR17","doi-asserted-by":"publisher","first-page":"106445","DOI":"10.1016\/j.knosys.2020.106445","volume":"210","author":"YJ Tian","year":"2020","unstructured":"Tian YJ, Fu SJ (2020) A descriptive framework for the field of deep learning applications in medical images. Knowl-Based Syst 210:106445. https:\/\/doi.org\/10.1016\/j.knosys.2020.106445","journal-title":"Knowl-Based Syst"},{"key":"168_CR18","doi-asserted-by":"publisher","first-page":"101718","DOI":"10.1016\/j.compmedimag.2020.101718","volume":"82","author":"R Castro-Zunti","year":"2020","unstructured":"Castro-Zunti R, Park EH, Choi Y, Jin GY, Ko SB (2020) Early detection of ankylosing spondylitis using texture features and statistical machine learning, and deep learning, with some patient age analysis. Comput Med Imaging Graph 82:101718. https:\/\/doi.org\/10.1016\/j.compmedimag.2020.101718","journal-title":"Comput Med Imaging Graph"},{"issue":"3","key":"168_CR19","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1148\/radiol.212182","volume":"304","author":"RY Kim","year":"2022","unstructured":"Kim RY, Oke JL, Pickup LC, Munden RF, Dotson TL, Bellinger CR et al (2022) Artificial intelligence tool for assessment of indeterminate pulmonary nodules detected with CT. Radiology 304(3):683\u2013691. https:\/\/doi.org\/10.1148\/radiol.212182","journal-title":"Radiology"},{"key":"168_CR20","doi-asserted-by":"publisher","unstructured":"He KM, Zhang XY, Ren SQ, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE conference on computer vision and pattern recognition, IEEE, Las Vegas, 27-30 June 2016. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"168_CR21","doi-asserted-by":"publisher","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the 2018 IEEE\/CVF conference on computer vision and pattern recognition, IEEE, Salt Lake City, 18-23 June 2018. https:\/\/doi.org\/10.1109\/CVPR.2018.00745","DOI":"10.1109\/CVPR.2018.00745"},{"key":"168_CR22","doi-asserted-by":"publisher","unstructured":"Woo S, Park J, Lee JY, Kweon IS (2018) CBAM: Convolutional block attention module. In: Proceedings of the 15th European Conference on Computer Vision, Springer, Munich, 8-14 September 2018. https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"168_CR23","doi-asserted-by":"publisher","unstructured":"Chen YP, Dai XY, Liu MC, Chen DD, Yuan L, Liu ZC (2020) Dynamic convolution: Attention over convolution kernels. In: Proceedings of the 2020 IEEE\/CVF conference on computer vision and pattern recognition, IEEE, Seattle, 13-19 June 2020. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01104","DOI":"10.1109\/CVPR42600.2020.01104"},{"key":"168_CR24","unstructured":"Li C, Zhou AJ, Yao AB (2022) Omni-dimensional dynamic convolution. arXiv preprint arXiv: 2209.07947"},{"key":"168_CR25","doi-asserted-by":"publisher","unstructured":"Hou QB, Zhou DQ, Feng JS (2021) Coordinate attention for efficient mobile network design. In: Proceedings of the 2021 IEEE\/CVF conference on computer vision and pattern recognition, IEEE, Nashville, 20-25 June 2021. https:\/\/doi.org\/10.1109\/CVPR46437.2021.01350","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"168_CR26","doi-asserted-by":"publisher","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","volume":"8","author":"MEH Chowdhury","year":"2020","unstructured":"Chowdhury MEH, Rahman T, Khandakar A, Mazhar R, Kadir MA, Mahbub ZB et al (2020) Can AI help in screening viral and COVID-19 pneumonia? IEEE Access 8:132665\u2013132676. https:\/\/doi.org\/10.1109\/ACCESS.2020.3010287","journal-title":"IEEE Access"},{"key":"168_CR27","doi-asserted-by":"publisher","first-page":"104319","DOI":"10.1016\/j.compbiomed.2021.104319","volume":"132","author":"T Rahman","year":"2021","unstructured":"Rahman T, Khandakar A, Qiblawey Y, Tahir A, Kiranyaz S, Abul Kashem SB et al (2021) Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Comput Biol Med 132:104319. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104319","journal-title":"Comput Biol Med"},{"key":"168_CR28","doi-asserted-by":"publisher","unstructured":"Cohen JP, Morrison P, Dao L, Roth K, Duong TQ, Ghassemi M (2020) COVID-19 image data collection: Prospective predictions are the future. arXiv preprint arXiv 2006.11988. https:\/\/doi.org\/10.59275\/j.melba.2020-48g7","DOI":"10.59275\/j.melba.2020-48g7"},{"key":"168_CR29","doi-asserted-by":"publisher","unstructured":"Kermany D, Zhang K, Goldbaum M (2018) Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification. Mendeley Data. https:\/\/doi.org\/10.17632\/rscbjbr9sj.2","DOI":"10.17632\/rscbjbr9sj.2"},{"issue":"1","key":"168_CR30","doi-asserted-by":"publisher","first-page":"19549","DOI":"10.1038\/s41598-020-76550-z","volume":"10","author":"LD Wang","year":"2020","unstructured":"Wang LD, Lin ZQ, Wong A (2020) COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep 10(1):19549. https:\/\/doi.org\/10.1038\/s41598-020-76550-z","journal-title":"Sci Rep"},{"key":"168_CR31","doi-asserted-by":"publisher","first-page":"103792","DOI":"10.1016\/j.compbiomed.2020.103792","volume":"121","author":"T Ozturk","year":"2020","unstructured":"Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 121:103792. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103792","journal-title":"Comput Biol Med"},{"key":"168_CR32","doi-asserted-by":"publisher","unstructured":"Liu Z, Mao HZ, Wu CY, Feichtenhofer C, Darrell T, Xie SN (2022) A ConvNet for the 2020s. In: Proceedings of the 2022 IEEE\/CVF conference on computer vision and pattern recognition, IEEE, New Orleans, 18-24 June 2022. https:\/\/doi.org\/10.1109\/CVPR52688.2022.01167","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"168_CR33","doi-asserted-by":"publisher","unstructured":"Zhang XY, Zhou XY, Lin MX, Sun J (2018) ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the 2018 IEEE\/CVF conference on computer vision and pattern recognition, IEEE, Salt Lake City, 18-23 June 2018. https:\/\/doi.org\/10.1109\/CVPR.2018.00716","DOI":"10.1109\/CVPR.2018.00716"},{"key":"168_CR34","doi-asserted-by":"publisher","unstructured":"Sandler M, Howard A, Zhu ML, Zhmoginov A, Chen LC (2018) MobileNetV2: Inverted residuals and linear bottlenecks. In: Proceedings of the 2018 IEEE\/CVF conference on computer vision and pattern recognition, IEEE, Salt Lake City, 18-23 June 2018. https:\/\/doi.org\/10.1109\/CVPR.2018.00474","DOI":"10.1109\/CVPR.2018.00474"},{"key":"168_CR35","unstructured":"Tan MX, Le QV (2019) EfficientNet: Rethinking model scaling for convolutional neural networks. In: Proceedings of the 36th international conference on machine learning, IMLS, Long Beach, California, 9-15 June 2019"},{"issue":"2","key":"168_CR36","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s10278-021-00556-w","volume":"35","author":"JJ Jeong","year":"2022","unstructured":"Jeong JJ, Tariq A, Adejumo T, Trivedi H, Gichoya JW, Banerjee I (2022) Systematic review of generative adversarial networks (GANs) for medical image classification and segmentation. J Digit Imaging 35(2):137\u2013152. https:\/\/doi.org\/10.1007\/s10278-021-00556-w","journal-title":"J Digit Imaging"}],"updated-by":[{"DOI":"10.1186\/s42492-024-00170-x","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000}}],"container-title":["Visual Computing for Industry, Biomedicine, and Art"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42492-024-00168-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s42492-024-00168-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42492-024-00168-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T08:08:24Z","timestamp":1721722104000},"score":1,"resource":{"primary":{"URL":"https:\/\/vciba.springeropen.com\/articles\/10.1186\/s42492-024-00168-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,8]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["168"],"URL":"https:\/\/doi.org\/10.1186\/s42492-024-00168-5","relation":{"correction":[{"id-type":"doi","id":"10.1186\/s42492-024-00170-x","asserted-by":"object"}]},"ISSN":["2524-4442"],"issn-type":[{"value":"2524-4442","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,8]]},"assertion":[{"value":"8 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2024","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1186\/s42492-024-00170-x","URL":"https:\/\/doi.org\/10.1186\/s42492-024-00170-x","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"17"}}