{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:26:30Z","timestamp":1778081190736,"version":"3.51.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T00:00:00Z","timestamp":1649721600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T00:00:00Z","timestamp":1649721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2017YFB0202602"],"award-info":[{"award-number":["2017YFB0202602"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Health Inf Sci Syst"],"DOI":"10.1007\/s13755-022-00174-y","type":"journal-article","created":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T09:02:40Z","timestamp":1649754160000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["MFDNN: multi-channel feature deep neural network algorithm to identify COVID19 chest X-ray images"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0565-4217","authenticated-orcid":false,"given":"Liangrui","family":"Pan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boya","family":"Ji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hetian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingting","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mitchai","family":"Chongcheawchamnan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaolaing","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,12]]},"reference":[{"issue":"15","key":"174_CR1","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1093\/cid\/ciaa207","volume":"71","author":"J Xu","year":"2020","unstructured":"Xu J, et al. Computed tomographic imaging of 3 patients with coronavirus disease 2019 pneumonia with negative virus real-time reverse-transcription polymerase chain reaction test. Clin Infect Dis. 2020;71(15):850\u20132. https:\/\/doi.org\/10.1093\/cid\/ciaa207.","journal-title":"Clin Infect Dis"},{"issue":"10","key":"174_CR2","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.eng.2020.04.010","volume":"6","author":"X Xu","year":"2020","unstructured":"Xu X, et al. A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering. 2020;6(10):1122\u20139. https:\/\/doi.org\/10.1016\/j.eng.2020.04.010.","journal-title":"Engineering"},{"issue":"8","key":"174_CR3","doi-asserted-by":"publisher","first-page":"2688","DOI":"10.1109\/TMI.2020.2993291","volume":"39","author":"Y Oh","year":"2020","unstructured":"Oh Y, Park S, Ye JC. Deep learning COVID-19 features on CXR using limited training data sets. IEEE Trans Med Imaging. 2020;39(8):2688\u2013700. https:\/\/doi.org\/10.1109\/TMI.2020.2993291.","journal-title":"IEEE Trans Med Imaging"},{"issue":"8","key":"174_CR4","doi-asserted-by":"publisher","first-page":"2676","DOI":"10.1109\/TMI.2020.2994459","volume":"39","author":"S Roy","year":"2020","unstructured":"Roy S, et al. Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound. IEEE Trans Med Imaging. 2020;39(8):2676\u201387. https:\/\/doi.org\/10.1109\/TMI.2020.2994459.","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"174_CR5","doi-asserted-by":"publisher","first-page":"2000775","DOI":"10.1183\/13993003.00775-2020","volume":"56","author":"S Wang","year":"2020","unstructured":"Wang S, et al. A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur Respir J. 2020;56(2):2000775. https:\/\/doi.org\/10.1183\/13993003.00775-2020.","journal-title":"Eur Respir J"},{"issue":"1","key":"174_CR6","doi-asserted-by":"publisher","first-page":"9887","DOI":"10.1038\/s41598-021-88807-2","volume":"11","author":"A Zargari Khuzani","year":"2021","unstructured":"Zargari Khuzani A, Heidari M, Shariati SA. COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images. Sci Rep. 2021;11(1):9887. https:\/\/doi.org\/10.1038\/s41598-021-88807-2.","journal-title":"Sci Rep"},{"key":"174_CR7","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab226","author":"X Wang","year":"2021","unstructured":"Wang X, et al. DeepR2cov: deep representation learning on heterogeneous drug networks to discover anti-inflammatory agents for COVID-19. Brief Bioinform. 2021. https:\/\/doi.org\/10.1093\/bib\/bbab226.","journal-title":"Brief Bioinform"},{"key":"174_CR8","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/B978-0-12-818438-7.00002-2","volume-title":"Artificial intelligence in healthcare","author":"A Bohr","year":"2020","unstructured":"Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. In: Artificial intelligence in healthcare. New York: Elsevier; 2020. p. 25\u201360. https:\/\/doi.org\/10.1016\/B978-0-12-818438-7.00002-2."},{"key":"174_CR9","doi-asserted-by":"publisher","unstructured":"P. Daniel et al. Artificially intelligent medical assistant robot: automating data collection and diagnostics for medical practitioners. 2021. https:\/\/doi.org\/10.13016\/A9OZ-0OE7","DOI":"10.13016\/A9OZ-0OE7"},{"issue":"12","key":"174_CR10","doi-asserted-by":"publisher","first-page":"1988","DOI":"10.1007\/s10439-018-2095-6","volume":"46","author":"Y Du","year":"2018","unstructured":"Du Y, et al. Classification of tumor epithelium and stroma by exploiting image features learned by deep convolutional neural networks. Ann Biomed Eng. 2018;46(12):1988\u201399. https:\/\/doi.org\/10.1007\/s10439-018-2095-6.","journal-title":"Ann Biomed Eng"},{"issue":"3","key":"174_CR11","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aaa1ca","volume":"63","author":"M Heidari","year":"2018","unstructured":"Heidari M, et al. Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm. Phys Med Biol. 2018;63(3): 035020. https:\/\/doi.org\/10.1088\/1361-6560\/aaa1ca.","journal-title":"Phys Med Biol"},{"issue":"2","key":"174_CR12","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3390\/s18010018","volume":"18","author":"P ThanhNoi","year":"2017","unstructured":"ThanhNoi P, Kappas M. Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using sentinel-2 imagery. Sensors. 2017;18(2):18. https:\/\/doi.org\/10.3390\/s18010018.","journal-title":"Sensors"},{"key":"174_CR13","doi-asserted-by":"publisher","first-page":"427","DOI":"10.3389\/fmed.2020.00427","volume":"7","author":"SH Yoo","year":"2020","unstructured":"Yoo SH, et al. Deep learning-based decision-tree classifier for COVID-19 diagnosis from chest X-ray imaging. Front Med. 2020;7:427. https:\/\/doi.org\/10.3389\/fmed.2020.00427.","journal-title":"Front Med"},{"issue":"8","key":"174_CR14","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1007\/s11739-020-02475-0","volume":"15","author":"D Assaf","year":"2020","unstructured":"Assaf D, et al. Utilization of machine-learning models to accurately predict the risk for critical COVID-19. Intern Emerg Med. 2020;15(8):1435\u201343. https:\/\/doi.org\/10.1007\/s11739-020-02475-0.","journal-title":"Intern Emerg Med"},{"issue":"6","key":"174_CR15","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0235187","volume":"15","author":"MA Elaziz","year":"2020","unstructured":"Elaziz MA, Hosny KM, Salah A, Darwish MM, Lu S, Sahlol AT. New machine learning method for image-based diagnosis of COVID-19. PLoS ONE. 2020;15(6): e0235187. https:\/\/doi.org\/10.1371\/journal.pone.0235187.","journal-title":"PLoS ONE"},{"issue":"1","key":"174_CR16","doi-asserted-by":"publisher","first-page":"19549","DOI":"10.1038\/s41598-020-76550-z","volume":"10","author":"L Wang","year":"2020","unstructured":"Wang L, Lin ZQ, Wong A. COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep. 2020;10(1):19549. https:\/\/doi.org\/10.1038\/s41598-020-76550-z.","journal-title":"Sci Rep"},{"key":"174_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-021-00984-y","author":"A Narin","year":"2021","unstructured":"Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Anal Applic. 2021. https:\/\/doi.org\/10.1007\/s10044-021-00984-y.","journal-title":"Pattern Anal Applic"},{"issue":"2","key":"174_CR18","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1007\/s13246-020-00865-4","volume":"43","author":"ID Apostolopoulos","year":"2020","unstructured":"Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. 2020;43(2):635\u201340. https:\/\/doi.org\/10.1007\/s13246-020-00865-4.","journal-title":"Phys Eng Sci Med"},{"key":"174_CR19","unstructured":"Hemdan EE-D, Shouman MA, Karar ME. COVIDX-Net: a framework of deep learning classifiers to diagnose COVID-19 in X-ray images. arXiv:2003.11055 [cs, eess], 2020, Accessed: 03, 2021. [Online]. Available: http:\/\/arxiv.org\/abs\/2003.11055"},{"key":"174_CR20","doi-asserted-by":"publisher","unstructured":"Vasudevan A, Anderson A, Gregg D. Parallel multi channel convolution using general matrix multiplication. In: 2017 IEEE 28th International Conference on Application-specific Systems, Architectures and Processors (ASAP), Seattle, WA, USA, 2017, pp. 19\u201324. https:\/\/doi.org\/10.1109\/ASAP.2017.7995254.","DOI":"10.1109\/ASAP.2017.7995254"},{"key":"174_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.optcom.2021.127110","volume":"495","author":"S Yang","year":"2021","unstructured":"Yang S, et al. Multi-channel multi-task optical performance monitoring based multi-input multi-output deep learning and transfer learning for SDM. Opt Commun. 2021;495: 127110. https:\/\/doi.org\/10.1016\/j.optcom.2021.127110.","journal-title":"Opt Commun"},{"issue":"10","key":"174_CR22","doi-asserted-by":"publisher","first-page":"4321","DOI":"10.3390\/app11104321","volume":"11","author":"B Yang","year":"2021","unstructured":"Yang B, Xiao Z. A multi-channel and multi-spatial attention convolutional neural network for prostate cancer ISUP grading. Appl Sci. 2021;11(10):4321. https:\/\/doi.org\/10.3390\/app11104321.","journal-title":"Appl Sci"},{"issue":"8","key":"174_CR23","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/LGRS.2019.2894399","volume":"16","author":"X Liu","year":"2019","unstructured":"Liu X, Zhou Y, Zhao J, Yao R, Liu B, Zheng Y. Siamese convolutional neural networks for remote sensing scene classification. IEEE Geosci Remote Sens Lett. 2019;16(8):1200\u20134. https:\/\/doi.org\/10.1109\/LGRS.2019.2894399.","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"2","key":"174_CR24","doi-asserted-by":"publisher","first-page":"235","DOI":"10.35741\/issn.0258-2724.56.2.19","volume":"56","author":"F Arifin","year":"2021","unstructured":"Arifin F, Artanto Nurhasanah H, Gunawan TS. Fast COVID-19 detection of chest X-ray images using single shot detection MobileNet convolutional neural networks. J Southwest Jiaotong Univ. 2021;56(2):235\u201348. https:\/\/doi.org\/10.35741\/issn.0258-2724.56.2.19.","journal-title":"J Southwest Jiaotong Univ"},{"key":"174_CR25","doi-asserted-by":"publisher","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","volume":"8","author":"MEH Chowdhury","year":"2020","unstructured":"Chowdhury MEH, et al. Can AI help in screening viral and COVID-19 pneumonia? IEEE Access. 2020;8:132665\u201376. https:\/\/doi.org\/10.1109\/ACCESS.2020.3010287.","journal-title":"IEEE Access"},{"key":"174_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106852","volume":"98","author":"F Shen","year":"2021","unstructured":"Shen F, Zhao X, Kou G, Alsaadi FE. A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique. Appl Soft Comput. 2021;98: 106852. https:\/\/doi.org\/10.1016\/j.asoc.2020.106852.","journal-title":"Appl Soft Comput"},{"key":"174_CR27","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.ins.2017.05.008","volume":"409\u2013410","author":"W-C Lin","year":"2017","unstructured":"Lin W-C, Tsai C-F, Hu Y-H, Jhang J-S. Clustering-based undersampling in class-imbalanced data. Inf Sci. 2017;409\u2013410:17\u201326. https:\/\/doi.org\/10.1016\/j.ins.2017.05.008.","journal-title":"Inf Sci"},{"key":"174_CR28","doi-asserted-by":"publisher","first-page":"114986","DOI":"10.1016\/j.eswa.2021.114986","volume":"178","author":"A \u00d6zdemir","year":"2021","unstructured":"\u00d6zdemir A, Polat K, Alhudhaif A. Classification of imbalanced hyperspectral images using SMOTE-based deep learning methods. Expert Syst Appl. 2021;178:114986. https:\/\/doi.org\/10.1016\/j.eswa.2021.114986.","journal-title":"Expert Syst Appl"},{"key":"174_CR29","doi-asserted-by":"publisher","unstructured":"Chang W-G, You T, Seo S, Kwak S, Han B. Domain-specific batch normalization for unsupervised domain adaptation. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 7346\u20137354. https:\/\/doi.org\/10.1109\/CVPR.2019.00753.","DOI":"10.1109\/CVPR.2019.00753"},{"key":"174_CR30","doi-asserted-by":"publisher","unstructured":"Hara K, Saito D, Shouno H. Analysis of function of rectified linear unit used in deep learning. In: 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, 2015, pp. 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN.2015.7280578.","DOI":"10.1109\/IJCNN.2015.7280578"},{"issue":"7","key":"174_CR31","doi-asserted-by":"publisher","first-page":"1693","DOI":"10.3390\/s19071693","volume":"19","author":"C Gong","year":"2019","unstructured":"Gong C, et al. A novel deep learning method for intelligent fault diagnosis of rotating machinery based on improved CNN-SVM and multichannel data fusion. Sensors. 2019;19(7):1693. https:\/\/doi.org\/10.3390\/s19071693.","journal-title":"Sensors"},{"key":"174_CR32","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.sysarc.2019.02.008","volume":"95","author":"T-Y Hsiao","year":"2019","unstructured":"Hsiao T-Y, Chang Y-C, Chou H-H, Chiu C-T. Filter-based deep-compression with global average pooling for convolutional networks. J Syst Architect. 2019;95:9\u201318. https:\/\/doi.org\/10.1016\/j.sysarc.2019.02.008.","journal-title":"J Syst Architect"},{"issue":"9","key":"174_CR33","doi-asserted-by":"publisher","first-page":"10834","DOI":"10.1109\/JSEN.2021.3059849","volume":"21","author":"L Pan","year":"2021","unstructured":"Pan L, Pipitsunthonsan P, Daengngam C, Channumsin S, Sreesawet S, Chongcheawchamnan M. Identification of complex mixtures for raman spectroscopy using a novel scheme based on a new multi-label deep neural network. IEEE Sensors J. 2021;21(9):10834\u201343. https:\/\/doi.org\/10.1109\/JSEN.2021.3059849.","journal-title":"IEEE Sensors J"}],"container-title":["Health Information Science and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-022-00174-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13755-022-00174-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-022-00174-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T13:28:22Z","timestamp":1669814902000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13755-022-00174-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,12]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["174"],"URL":"https:\/\/doi.org\/10.1007\/s13755-022-00174-y","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.08.04.21261235","asserted-by":"object"}]},"ISSN":["2047-2501"],"issn-type":[{"value":"2047-2501","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,12]]},"assertion":[{"value":"13 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}],"article-number":"4"}}