{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T14:02:50Z","timestamp":1783173770757,"version":"3.54.6"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s00521-022-07274-6","type":"journal-article","created":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T15:02:51Z","timestamp":1651676571000},"page":"16157-16168","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Deep transfer learning-based visual classification of pressure injuries stages"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3060-0432","authenticated-orcid":false,"given":"Betul","family":"Ay","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beyda","family":"Tasar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeynep","family":"Utlu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevser","family":"Ay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Galip","family":"Aydin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,5,4]]},"reference":[{"issue":"1","key":"7274_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1177\/193229680900300101","volume":"3","author":"DC Klonoff","year":"2009","unstructured":"Klonoff DC (2009) The increasing incidence of diabetes in the 21st century. J Diabetes Sci Technol 3(1):1\u20132. https:\/\/doi.org\/10.1177\/193229680900300101","journal-title":"J Diabetes Sci Technol"},{"key":"7274_CR2","unstructured":"Pieper B (2015) Pressure ulcers: impact, etiology, and classification. In: Acute and chronic wounds: current management concepts, pp 124\u2013139"},{"key":"7274_CR3","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1111\/j.1532-5415.2004.52106.x","volume":"52","author":"SD Horn","year":"2004","unstructured":"Horn SD, Bender SA, Ferguson ML et al (2004) The national pressure ulcer long-term care study: pressure ulcer development in long-term care residents. J Am Geriatr Soc 52:359\u2013367","journal-title":"J Am Geriatr Soc"},{"key":"7274_CR4","unstructured":"Berlowitz D, Vandeusen Lukas C, Parker V et al (2018) Preventing pressure ulcers in  Hospitals. Agency for Healthcare Research and Quality, Rockville, MD. https:\/\/www.ahrq.gov\/patientsafety\/settings\/hospital\/resource\/pressureulcer\/tool\/index.html"},{"key":"7274_CR5","first-page":"42","volume":"49","author":"G Brown","year":"2003","unstructured":"Brown G (2003) Long-term outcomes of full-thickness pressure ulcers: healing and mortality. Ostomy Wound Manage 49:42\u201350","journal-title":"Ostomy Wound Manage"},{"issue":"7","key":"7274_CR6","first-page":"201","volume":"19","author":"JJ Soldevilla Agreda","year":"2007","unstructured":"Soldevilla Agreda JJ et al (2007) The burden of pressure ulcers in Spain. Wounds Compend Clin Res Pract 19(7):201\u2013206","journal-title":"Wounds Compend Clin Res Pract"},{"key":"7274_CR7","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1177\/1054773817705541","volume":"27","author":"A Tubaishat","year":"2018","unstructured":"Tubaishat A, Papanikolaou P, Anthony D, Habiballah L (2018) Pressure ulcers prevalence in the acute care setting: a systematic review, 2000\u20132015. Clin Nurs Res 27:643\u2013659. https:\/\/doi.org\/10.1177\/1054773817705541","journal-title":"Clin Nurs Res"},{"key":"7274_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2019.101742","volume":"102","author":"S Zahia","year":"2020","unstructured":"Zahia S, Garcia Zapirain MB, Sevillano X et al (2020) Pressure injury image analysis with machine learning techniques: a systematic review on previous and possible future methods. Artif Intell Med 102:101742. https:\/\/doi.org\/10.1016\/j.artmed.2019.101742","journal-title":"Artif Intell Med"},{"key":"7274_CR9","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1097\/WON.0000000000000281","volume":"43","author":"LE Edsberg","year":"2016","unstructured":"Edsberg LE, Black JM, Goldberg M et al (2016) Revised national pressure ulcer advisory panel pressure injury staging system: revised pressure injury staging system. J Wound Ostomy Cont Nurs 43:585","journal-title":"J Wound Ostomy Cont Nurs"},{"key":"7274_CR10","first-page":"2587","volume":"7","author":"Q Jiang","year":"2014","unstructured":"Jiang Q, Li X, Qu X et al (2014) The incidence, risk factors and characteristics of pressure ulcers in hospitalized patients in China. Int J Clin Exp Pathol 7:2587","journal-title":"Int J Clin Exp Pathol"},{"key":"7274_CR11","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.jtv.2018.05.001","volume":"27","author":"M Kasikci","year":"2018","unstructured":"Kasikci M, Aksoy M, Ay E (2018) Investigation of the prevalence of pressure ulcers and patient-related risk factors in hospitals in the province of Erzurum: a cross-sectional study. J Tissue Viabil 27:135\u2013140. https:\/\/doi.org\/10.1016\/j.jtv.2018.05.001","journal-title":"J Tissue Viabil"},{"key":"7274_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12938-016-0298-3","volume":"16","author":"OP David","year":"2017","unstructured":"David OP, Sierra-Sosa D, Zapirain BG (2017) Pressure ulcer image segmentation technique through synthetic frequencies generation and contrast variation using toroidal geometry. Biomed Eng Online 16:1\u201319. https:\/\/doi.org\/10.1186\/s12938-016-0298-3","journal-title":"Biomed Eng Online"},{"key":"7274_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/IEMBS.2006.259513","author":"J Leachtenauer","year":"2006","unstructured":"Leachtenauer J, Kell S, Turner B et al (2006) A non-contact imaging-based approach to detecting stage pressure ulcers. Annu Int Conf IEEE Eng Med Biol Proc. https:\/\/doi.org\/10.1109\/IEMBS.2006.259513","journal-title":"Annu Int Conf IEEE Eng Med Biol Proc"},{"key":"7274_CR14","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1134\/S1054661814020084","volume":"24","author":"R Guadagnin","year":"2014","unstructured":"Guadagnin R, De Neves RS, Santana LA, Guilhem DB (2014) An image mining based approach to detect pressure ulcer stage. Pattern Recognit Image Anal 24:292\u2013296. https:\/\/doi.org\/10.1134\/S1054661814020084","journal-title":"Pattern Recognit Image Anal"},{"key":"7274_CR15","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1109\/42.552057","volume":"16","author":"GL Hansen","year":"1997","unstructured":"Hansen GL, Sparrow EM, Kokate JY et al (1997) Wound status evaluation using color image processing. IEEE Trans Med Imaging 16:78\u201386. https:\/\/doi.org\/10.1109\/42.552057","journal-title":"IEEE Trans Med Imaging"},{"key":"7274_CR16","doi-asserted-by":"publisher","first-page":"68","DOI":"10.15623\/ijret.2013.0207009","volume":"02","author":"NB Mankar","year":"2013","unstructured":"Mankar NB (2013) Comparision of different \u0131maging techniques used for chronic wounds. Int J Res Eng Technol 02:68\u201370. https:\/\/doi.org\/10.15623\/ijret.2013.0207009","journal-title":"Int J Res Eng Technol"},{"key":"7274_CR17","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany DS, Goldbaum M, Cai W et al (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172:1122\u20131131","journal-title":"Cell"},{"key":"7274_CR18","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.cmpb.2018.02.018","volume":"159","author":"S Zahia","year":"2018","unstructured":"Zahia S, Sierra-Sosa D, Garcia-Zapirain B, Elmaghraby A (2018) Tissue classification and segmentation of pressure injuries using convolutional neural networks. Comput Methods Programs Biomed 159:51\u201358","journal-title":"Comput Methods Programs Biomed"},{"key":"7274_CR19","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.compbiomed.2017.09.015","volume":"90","author":"B Garcia-Zapirain","year":"2017","unstructured":"Garcia-Zapirain B, Shalaby A, El-Baz A, Elmaghraby A (2017) Automated framework for accurate segmentation of pressure ulcer images. Comput Biol Med 90:137\u2013145","journal-title":"Comput Biol Med"},{"key":"7274_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105867","author":"RHL Silva","year":"2021","unstructured":"Silva RHL, Machado AMC (2021) Automatic measurement of pressure ulcers using support vector machines and grabcut. Comput Methods Progr Biomed. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105867","journal-title":"Comput Methods Progr Biomed"},{"key":"7274_CR21","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2018.8451119","author":"M Elmogy","year":"2018","unstructured":"Elmogy M, Garcia-Zapirain B, Burns C et al (2018) Tissues classification for pressure ulcer images based on 3D convolutional neural network. Proc Int Conf Image Process ICIP. https:\/\/doi.org\/10.1109\/ICIP.2018.8451119","journal-title":"Proc Int Conf Image Process ICIP"},{"key":"7274_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s20102933","volume":"20","author":"S Zahia","year":"2020","unstructured":"Zahia S, Garcia-Zapirain B, Elmaghraby A (2020) Integrating 3D model representation for an accurate non-invasive assessment of pressure injuries with deep learning. Sensors (Switzerland) 20:1\u201315. https:\/\/doi.org\/10.3390\/s20102933","journal-title":"Sensors (Switzerland)"},{"key":"7274_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2019.100162","volume":"17","author":"C Chakraborty","year":"2019","unstructured":"Chakraborty C (2019) Computational approach for chronic wound tissue characterization. Inform Med Unlocked 17:100162","journal-title":"Inform Med Unlocked"},{"key":"7274_CR24","doi-asserted-by":"publisher","DOI":"10.1109\/INISTA52262.2021.9548585","author":"B Yilmaz","year":"2021","unstructured":"Yilmaz B, Atagun E, Demircan FO, Yucedag I (2021) Classification of pressure ulcer images with logistic regression. Int Conf Innov Intell Syst Appl INISTA 2021 Proc. https:\/\/doi.org\/10.1109\/INISTA52262.2021.9548585","journal-title":"Int Conf Innov Intell Syst Appl INISTA 2021 Proc"},{"key":"7274_CR25","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/EMB.2007.901786","volume":"26","author":"DI Kosmopoulos","year":"2007","unstructured":"Kosmopoulos DI, Tzevelekou FL (2007) Automated pressure ulcer lesion diagnosis for telemedicine systems. IEEE Eng Med Biol Mag 26:18\u201322","journal-title":"IEEE Eng Med Biol Mag"},{"key":"7274_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/app10217613","volume":"10","author":"D Marijanovi\u0107","year":"2020","unstructured":"Marijanovi\u0107 D, Filko D (2020) A systematic overview of recent methods for non-contact chronic wound analysis. Appl Sci 10:1\u201328. https:\/\/doi.org\/10.3390\/app10217613","journal-title":"Appl Sci"},{"key":"7274_CR27","doi-asserted-by":"publisher","DOI":"10.1109\/CBMS.2015.33","author":"MVN Bedo","year":"2015","unstructured":"Bedo MVN, Santos LFD, Oliveira WD et al (2015) Color and texture influence on computer-aided diagnosis of dermatological ulcers. Proc IEEE Symp Comput Med Syst. https:\/\/doi.org\/10.1109\/CBMS.2015.33","journal-title":"Proc IEEE Symp Comput Med Syst"},{"key":"7274_CR28","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/851582","author":"R Mukherjee","year":"2014","unstructured":"Mukherjee R, Manohar DD, Das DK et al (2014) Automated tissue classification framework for reproducible chronic wound assessment. Biomed Res Int. https:\/\/doi.org\/10.1155\/2014\/851582","journal-title":"Biomed Res Int"},{"key":"7274_CR29","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1007\/s10916-015-0424-y","volume":"40","author":"C Chakraborty","year":"2016","unstructured":"Chakraborty C, Gupta B, Ghosh SK et al (2016) Telemedicine supported chronic wound tissue prediction using classification approaches. J Med Syst 40:68","journal-title":"J Med Syst"},{"key":"7274_CR30","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1109\/TMI.2009.2033595","volume":"29","author":"F Veredas","year":"2009","unstructured":"Veredas F, Mesa H, Morente L (2009) Binary tissue classification on wound images with neural networks and bayesian classifiers. IEEE Trans Med Imaging 29:410\u2013427","journal-title":"IEEE Trans Med Imaging"},{"key":"7274_CR31","doi-asserted-by":"crossref","unstructured":"Chakraborty C, Gupta B, Ghosh SK (2015) Chronic wound tissue characterization under telemedicine framework. In: 2015 17th \u0131nternational conference on E-health networking, application & services (HealthCom). pp 569\u2013573","DOI":"10.1109\/HealthCom.2015.7454566"},{"key":"7274_CR32","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1109\/TMI.2010.2077739","volume":"30","author":"H Wannous","year":"2010","unstructured":"Wannous H, Lucas Y, Treuillet S (2010) Enhanced assessment of the wound-healing process by accurate multiview tissue classification. IEEE Trans Med Imaging 30:315\u2013326","journal-title":"IEEE Trans Med Imaging"},{"key":"7274_CR33","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.neucom.2014.12.091","volume":"164","author":"FJ Veredas","year":"2015","unstructured":"Veredas FJ, Luque-Baena RM, Martin-Santos FJ et al (2015) Wound image evaluation with machine learning. Neurocomputing 164:112\u2013122","journal-title":"Neurocomputing"},{"key":"7274_CR34","doi-asserted-by":"publisher","first-page":"23002","DOI":"10.1117\/1.3378149","volume":"19","author":"H Wannous","year":"2010","unstructured":"Wannous H, Treuillet S, Lucas Y (2010) Robust tissue classification for reproducible wound assessment in telemedicine environments. J Electron Imaging 19:23002","journal-title":"J Electron Imaging"},{"key":"7274_CR35","doi-asserted-by":"crossref","unstructured":"Mesa H, Veredas FJ, Morente L (2008) A hybrid approach for tissue recognition on wound images. In: 2008 eighth \u0131nternational conference on hybrid \u0131ntelligent systems. pp 120\u2013125","DOI":"10.1109\/HIS.2008.33"},{"key":"7274_CR36","doi-asserted-by":"publisher","first-page":"2245","DOI":"10.1007\/s11517-018-1835-y","volume":"56","author":"B Garc\u00eda-Zapirain","year":"2018","unstructured":"Garc\u00eda-Zapirain B, Elmogy M, El-Baz A, Elmaghraby AS (2018) Classification of pressure ulcer tissues with 3D convolutional neural network. Med Biol Eng Comput 56:2245\u20132258. https:\/\/doi.org\/10.1007\/s11517-018-1835-y","journal-title":"Med Biol Eng Comput"},{"key":"7274_CR37","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.compbiomed.2015.02.015","volume":"60","author":"MF Ahmad Fauzi","year":"2015","unstructured":"Ahmad Fauzi MF, Khansa I, Catignani K et al (2015) Computerized segmentation and measurement of chronic wound images. Comput Biol Med 60:74\u201385. https:\/\/doi.org\/10.1016\/j.compbiomed.2015.02.015","journal-title":"Comput Biol Med"},{"key":"7274_CR38","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM.2018.8621130","author":"VN Shenoy","year":"2019","unstructured":"Shenoy VN, Foster E, Aalami L et al (2019) Deepwound: automated postoperative wound assessment and surgical site surveillance through convolutional neural networks. Proc 2018 IEEE Int Conf Bioinform Biomed BIBM 2018. https:\/\/doi.org\/10.1109\/BIBM.2018.8621130","journal-title":"Proc 2018 IEEE Int Conf Bioinform Biomed BIBM 2018"},{"key":"7274_CR39","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8461927","author":"H Nejati","year":"2018","unstructured":"Nejati H, Ghazijahani HA, Abdollahzadeh M et al (2018) Fine-grained wound tissue analysis using deep neural network. ICASSP IEEE Int Conf Acoust Speech Signal Process Proc. https:\/\/doi.org\/10.1109\/ICASSP.2018.8461927","journal-title":"ICASSP IEEE Int Conf Acoust Speech Signal Process Proc"},{"key":"7274_CR40","doi-asserted-by":"publisher","unstructured":"V. Godeiro, J. S. Neto, B. Carvalho, B. Santana, J. Ferraz and R. Gama, (2018) Chronic wound t\u0131ssue class\u0131f\u0131cat\u0131on us\u0131ng convolut\u0131onal networks and color space reduction. IEEE 28th \u0131nternational workshop on machine learning for signal processing (MLSP), pp 1\u20136, https:\/\/doi.org\/10.1109\/MLSP.2018.8517026.","DOI":"10.1109\/MLSP.2018.8517026"},{"key":"7274_CR41","doi-asserted-by":"publisher","unstructured":"Pholberdee N, Pathompatai C, Taeprasartsit P (2018) Study of chronic wound \u0131mage segmentation: \u0131mpact of tissue type and color data augmentation. In: Proceeding 2018 15th Int Jt Conf Comput Sci Softw Eng JCSSE 2018, pp 1\u20136, https:\/\/doi.org\/10.1109\/JCSSE.2018.8457392","DOI":"10.1109\/JCSSE.2018.8457392"},{"key":"7274_CR42","unstructured":"Haesler E (2019) European Pressure ulcer advisory panel, national pressure \u0131njury advisory panel, and pan pacific \u0131njury alliance. In: Prev Treat Press ulcers\/injuries Clin Pract Guidel Int Guidel EPUAP\/NPIAP\/PPPIA"},{"key":"7274_CR43","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren S, He K, Girshick R, Sun J (2016) Faster R-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39:1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7274_CR44","unstructured":"Barone AVM, Haddow B, Germann U, Sennrich R (2017) Regularization techniques for fine-tuning in neural machine translation. arXiv Prepr arXiv:1707.09920"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07274-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07274-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07274-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T18:51:35Z","timestamp":1662058295000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07274-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,4]]},"references-count":44,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["7274"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07274-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,4]]},"assertion":[{"value":"4 May 2021","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":"4 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}