{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T03:04:43Z","timestamp":1775099083953,"version":"3.50.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T00:00:00Z","timestamp":1647302400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T00:00:00Z","timestamp":1647302400000},"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 Process Lett"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s11063-022-10782-0","type":"journal-article","created":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T15:02:53Z","timestamp":1647356573000},"page":"3705-3726","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["NFU-Net: An Automated Framework for the Detection of Neurotrophic Foot Ulcer Using Deep Convolutional Neural Network"],"prefix":"10.1007","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6898-7417","authenticated-orcid":false,"given":"Chandran","family":"Venkatesan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. G.","family":"Sumithra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.","family":"Murugappan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,15]]},"reference":[{"issue":"10","key":"10782_CR1","doi-asserted-by":"publisher","first-page":"S96","DOI":"10.1016\/s0926-9959(98)94891-7","volume":"11","author":"A Boulton","year":"1998","unstructured":"Boulton A (1998) Diabetic neuropathic foot ulcers. J Eur Acad Dermatology Venereol 11(10):S96. https:\/\/doi.org\/10.1016\/s0926-9959(98)94891-7","journal-title":"J Eur Acad Dermatology Venereol"},{"key":"10782_CR2","doi-asserted-by":"crossref","unstructured":"R Reardon, D Simring, B Kim, J Mortensen, D Williams, and A Leslie 2020 AJGP-05-2020-Focus-Reardon-Diabetic-Foot-Ulcer-WEB 49(5):250\u2013255","DOI":"10.31128\/AJGP-11-19-5161"},{"key":"10782_CR3","doi-asserted-by":"publisher","unstructured":"P Saeedi et al. 2019Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract 157: 107843. https:\/\/doi.org\/10.1016\/j.diabres.2019.107843.","DOI":"10.1016\/j.diabres.2019.107843"},{"issue":"3","key":"10782_CR4","doi-asserted-by":"publisher","first-page":"660","DOI":"10.2337\/dc06-2043","volume":"30","author":"P Ince","year":"2007","unstructured":"Ince P, Game FL, Jeffcoate WJ (2007) Rate of healing of neuropathic ulcers of the foot in diabetes and its relationship to ulcer duration and ulcer area. Diabetes Care 30(3):660\u2013663. https:\/\/doi.org\/10.2337\/dc06-2043","journal-title":"Diabetes Care"},{"issue":"3","key":"10782_CR5","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1016\/j.dsx.2021.03.030","volume":"15","author":"P Manickum","year":"2021","unstructured":"Manickum P, Mashamba-Thompson T, Naidoo R, Ramklass S, Madiba T (2021) Knowledge and practice of diabetic foot care\u2014A scoping review. Diabetes Metabolic Syndrome: Clin Res Rev 15(3):783\u2013793. https:\/\/doi.org\/10.1016\/j.dsx.2021.03.030","journal-title":"Diabetes Metabolic Syndrome: Clin Res Rev"},{"key":"10782_CR6","doi-asserted-by":"publisher","first-page":"110991","DOI":"10.1016\/j.biopha.2020.110991","volume":"133","author":"Y Wang","year":"2021","unstructured":"Wang Y et al (2021) An update on potential biomarkers for diagnosing diabetic foot ulcer at early stage. Biomed Pharmacother 133:110991. https:\/\/doi.org\/10.1016\/j.biopha.2020.110991","journal-title":"Biomed Pharmacother"},{"key":"10782_CR7","doi-asserted-by":"publisher","DOI":"10.2147\/cwcmr.s62919","author":"M Kanapathy","year":"2015","unstructured":"Kanapathy M, Portou M, Tsui J, Toby Richards T (2015) Diabetic foot ulcers in conjunction with lower limb lymphedema: pathophysiology and treatment procedures. Res Chronic Wound Care Manag. https:\/\/doi.org\/10.2147\/cwcmr.s62919","journal-title":"Res Chronic Wound Care Manag"},{"key":"10782_CR8","doi-asserted-by":"publisher","DOI":"10.1002\/ima.22598","author":"AKM Sujit Kumar Das","year":"2021","unstructured":"Sujit Kumar Das AKM, Roy P (2021) Recognition of ischaemia and infection in diabetic foot ulcer: A deep convolutional neural network based approach. J Imaging Syst Technol Int. https:\/\/doi.org\/10.1002\/ima.22598","journal-title":"J Imaging Syst Technol Int"},{"key":"10782_CR9","doi-asserted-by":"publisher","first-page":"S1","DOI":"10.12968\/jowc.2018.27.Sup5.S1","volume":"27","author":"K Ousey","year":"2018","unstructured":"Ousey K et al (2018) Identifying and treating foot ulcers in patients with diabetes: saving feet, legs and lives. J Wound Care 27:S1\u2013S52. https:\/\/doi.org\/10.12968\/jowc.2018.27.Sup5.S1","journal-title":"J Wound Care"},{"key":"10782_CR10","doi-asserted-by":"publisher","first-page":"107424","DOI":"10.1016\/j.patcog.2020.107424","volume":"110","author":"W Wu","year":"2021","unstructured":"Wu W, Tao D, Li H, Yang Z, Cheng J (2021) Deep features for person re-identification on metric learning. Pattern Recognit 110:107424. https:\/\/doi.org\/10.1016\/j.patcog.2020.107424","journal-title":"Pattern Recognit"},{"key":"10782_CR11","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.neucom.2020.02.096","volume":"399","author":"B Sheng","year":"2020","unstructured":"Sheng B, Li J, Xiao F, Yang W (2020) Multilayer deep features with multiple kernel learning for action recognition. Neurocomputing 399:65\u201374. https:\/\/doi.org\/10.1016\/j.neucom.2020.02.096","journal-title":"Neurocomputing"},{"issue":"5","key":"10782_CR12","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1109\/tetci.2018.2866254","volume":"4","author":"M Goyal","year":"2020","unstructured":"Goyal M, Reeves ND, Davison AK, Rajbhandari S (2020) DFUNet: convolutional neural networks for diabetic foot ulcer classification. IEEE Trans Emerg Top Comput Intell 4(5):728\u2013739. https:\/\/doi.org\/10.1109\/tetci.2018.2866254","journal-title":"IEEE Trans Emerg Top Comput Intell"},{"issue":"21\u201322","key":"10782_CR13","doi-asserted-by":"publisher","first-page":"15655","DOI":"10.1007\/s11042-019-07820-w","volume":"79","author":"L Alzubaidi","year":"2020","unstructured":"Alzubaidi L, Fadhel MA, Oleiwi SR, Al-Shamma O, Zhang J (2020) DFU_QUTNet: diabetic foot ulcer classification using novel deep convolutional neural network. Multimed Tools Appl 79(21\u201322):15655\u201315677. https:\/\/doi.org\/10.1007\/s11042-019-07820-w","journal-title":"Multimed Tools Appl"},{"issue":"6","key":"10782_CR14","doi-asserted-by":"publisher","first-page":"1991","DOI":"10.1016\/j.dsx.2020.10.009","volume":"14","author":"KV Kavitha","year":"2020","unstructured":"Kavitha KV, Deshpande SR, Pandit AP, Unnikrishnan AG (2020) Application of tele-podiatry in diabetic foot management: a series of illustrative cases. Diabetes Metab Syndr Clin Res Rev 14(6):1991\u20131995. https:\/\/doi.org\/10.1016\/j.dsx.2020.10.009","journal-title":"Diabetes Metab Syndr Clin Res Rev"},{"issue":"S1","key":"10782_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/dmrr.3272","volume":"36","author":"M Monteiro-Soares","year":"2020","unstructured":"Monteiro-Soares M et al (2020) Diabetic foot ulcer classifications: a critical review. Diabetes Metab Res Rev 36(S1):1\u201316. https:\/\/doi.org\/10.1002\/dmrr.3272","journal-title":"Diabetes Metab Res Rev"},{"key":"10782_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcjd.2020.05.017","author":"IG Costa","year":"2020","unstructured":"Costa IG, Tregunno D, Camargo-Plazas P (2020) Patients\u2019 journey toward engagement in self-management of diabetic foot ulcer in adults with types 1 and 2 diabetes: a constructivist grounded theory study. Can J Diabetes. https:\/\/doi.org\/10.1016\/j.jcjd.2020.05.017","journal-title":"Can J Diabetes"},{"key":"10782_CR17","doi-asserted-by":"publisher","first-page":"105376","DOI":"10.1016\/j.cmpb.2020.105376","volume":"191","author":"DYT Chino","year":"2020","unstructured":"Chino DYT, Scabora LC, Cazzolato MT, Jorge AES, Traina C, Traina AJM (2020) Segmenting skin ulcers and measuring the wound area using deep convolutional networks. Comput Methods Programs Biomed 191:105376. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105376","journal-title":"Comput Methods Programs Biomed"},{"key":"10782_CR18","doi-asserted-by":"publisher","unstructured":"Mu\u00f1oz PL, Rodr\u00edguez R Automatic Segmentation of Diabetic foot ulcer from Mask Region-Based Convolutional Neural Networks. J Biomed Res Clin Investig. https:\/\/doi.org\/10.31546\/2633-8653.1006.","DOI":"10.31546\/2633-8653.1006"},{"key":"10782_CR19","doi-asserted-by":"publisher","first-page":"104042","DOI":"10.1016\/j.compbiomed.2020.104042","volume":"126","author":"RB Kim","year":"2020","unstructured":"Kim RB et al (2020) Utilization of smartphone and tablet camera photographs to predict healing of diabetes-related foot ulcers. Comput. Biol. Med 126:104042. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.104042","journal-title":"Comput. Biol. Med"},{"key":"10782_CR20","doi-asserted-by":"publisher","first-page":"100139","DOI":"10.1016\/j.smhl.2020.100139","volume":"18","author":"H Nguyen","year":"2020","unstructured":"Nguyen H et al (2020) Machine learning models for synthesizing actionable care decisions on lower extremity wounds. Smart Heal. 18:100139. https:\/\/doi.org\/10.1016\/j.smhl.2020.100139","journal-title":"Smart Heal."},{"key":"10782_CR21","doi-asserted-by":"publisher","first-page":"103616","DOI":"10.1016\/j.compbiomed.2020.103616","volume":"117","author":"M Goyal","year":"2020","unstructured":"Goyal M, Reeves ND, Rajbhandari S, Ahmad N, Wang C, Yap MH (2020) Recognition of ischaemia and infection in diabetic foot ulcers: dataset and techniques. Comput Biol Med 117:103616. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103616","journal-title":"Comput Biol Med"},{"issue":"1","key":"10782_CR22","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.jcjd.2020.05.018","volume":"45","author":"KG Dawson","year":"2021","unstructured":"Dawson KG, Jin A, Summerskill M, Swann D (2021) Mobile diabetes telemedicine clinics for aboriginal first nation people with reported diabetes in british columbia. Can J Diabetes 45(1):89\u201395. https:\/\/doi.org\/10.1016\/j.jcjd.2020.05.018","journal-title":"Can J Diabetes"},{"issue":"1","key":"10782_CR23","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1177\/1932296819871270","volume":"14","author":"A Ilo","year":"2020","unstructured":"Ilo A, Romsi P, M\u00e4kel\u00e4 J (2020) Infrared thermography and vascular disorders in diabetic feet. J Diabetes Sci Technol 14(1):28\u201336. https:\/\/doi.org\/10.1177\/1932296819871270","journal-title":"J Diabetes Sci Technol"},{"key":"10782_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2020.103531","author":"L Carlos Padierna","year":"2020","unstructured":"Carlos Padierna L, Fabi\u00e1n Amador-Medina L, Olivia Murillo-Ortiz B, Villase\u00f1or-Mora C (2020) Classification method of peripheral arterial disease in patients with type 2 diabetes mellitus by infrared thermography and machine learning\u201d. Infrared Phys Technol. https:\/\/doi.org\/10.1016\/j.infrared.2020.103531","journal-title":"Infrared Phys. Technol."},{"key":"10782_CR25","doi-asserted-by":"publisher","first-page":"103219","DOI":"10.1016\/j.infrared.2020.103219","volume":"105","author":"J Saminathan","year":"2020","unstructured":"Saminathan J, Sasikala M, Narayanamurthy VB, Rajesh K, Arvind R (2020) Computer aided detection of diabetic foot ulcer using asymmetry analysis of texture and temperature features. Infrared Phys. Technol. 105:103219. https:\/\/doi.org\/10.1016\/j.infrared.2020.103219","journal-title":"Infrared Phys. Technol."},{"key":"10782_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103744","author":"ACBH Ferreira","year":"2020","unstructured":"Ferreira ACBH, Ferreira DD, Oliveira HC, de Resende IC, Anjos A, de Lopes MHB (2020) Competitive neural layer-based method to identify people with high risk for diabetic foot. Comput Biol Med. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103744","journal-title":"Comput. Biol. Med."},{"key":"10782_CR27","unstructured":"M Goyal and S Hassanpour A Refined Deep Learning Architecture for Diabetic Foot Ulcers Detection, pp. 1\u20138, 2020, [Online]. Available: http:\/\/arxiv.org\/abs\/2007.07922."},{"issue":"4","key":"10782_CR28","doi-asserted-by":"publisher","first-page":"1730","DOI":"10.1109\/JBHI.2018.2868656","volume":"23","author":"M Goyal","year":"2019","unstructured":"Goyal M, Reeves ND, Rajbhandari S, Yap MH (2019) Robust methods for real-time diabetic foot ulcer detection and localization on mobile devices. IEEE J Biomed Heal Inform 23(4):1730\u20131741. https:\/\/doi.org\/10.1109\/JBHI.2018.2868656","journal-title":"IEEE J Biomed Heal Inform"},{"key":"10782_CR29","doi-asserted-by":"publisher","unstructured":"AL Da Costa Oliveira, AB De Carvalho, and DO Dantas 2021 Faster R-CNN approach for diabetic foot ulcer detection. In: VISIGRAPP 2021\u2014Proc. 16th Int. Jt. Conf. Comput. Vision, Imaging Comput. Graph. Theory Appl. 4(Visigrapp): 677\u2013684. https:\/\/doi.org\/10.5220\/0010255506770684.","DOI":"10.5220\/0010255506770684"},{"key":"10782_CR30","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-14-106","author":"R Blagus","year":"2013","unstructured":"Blagus R, Lusa L (2013) SMOTE for high-dimensional class-imbalanced data\u201d. BMC Bioinform. https:\/\/doi.org\/10.1186\/1471-2105-14-106","journal-title":"BMC Bioinform"},{"key":"10782_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.10.068","author":"BB Joris Gu\u00e9rin","year":"2020","unstructured":"Joris Gu\u00e9rin BB, Thiery S, Nyiri E, Gibaru O (2020) Combining pretrained CNN feature extractors to enhance clustering of complex natural images. Neurocomputing. https:\/\/doi.org\/10.1016\/j.neucom.2020.10.068","journal-title":"Neurocomputing"},{"key":"10782_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106849","author":"C Li","year":"2021","unstructured":"Li C, Yang Y, Liang H, Wu B (2021) Transfer learning for establishment ofrecognition ofCOVID-19 on CT imaging using small-sized training datasets. Knowledge-Based Syst J. https:\/\/doi.org\/10.1016\/j.knosys.2021.106849","journal-title":"Knowledge-Based Syst J"},{"key":"10782_CR33","doi-asserted-by":"publisher","first-page":"106912","DOI":"10.1016\/j.asoc.2020.106912","volume":"98","author":"MF Aslan","year":"2021","unstructured":"Aslan MF, Unlersen MF, Sabanci K, Durdu A (2021) CNN-based transfer learning\u2013BiLSTM network: a novel approach for COVID-19 infection detection. Appl Soft Comput 98:106912. https:\/\/doi.org\/10.1016\/j.asoc.2020.106912","journal-title":"Appl Soft Comput"},{"key":"10782_CR34","doi-asserted-by":"publisher","first-page":"2294","DOI":"10.1016\/j.procs.2020.04.248","volume":"171","author":"KO Mohammed Aarif","year":"2020","unstructured":"Mohammed Aarif KO, Poruran S (2020) OCR-Nets: variants of Pre-trained CNN for Urdu Handwritten character recognition via transfer learning. Procedia Comput Sci 171:2294\u20132301. https:\/\/doi.org\/10.1016\/j.procs.2020.04.248","journal-title":"Procedia Comput Sci"},{"key":"10782_CR35","doi-asserted-by":"publisher","unstructured":"Y Jia et al., \u201cCaffe: Convolutional architecture for fast feature embedding. In: MM 2014\u2014Proc. 2014 ACM Conf. Multimed. pp. 675\u2013678, 2014. https:\/\/doi.org\/10.1145\/2647868.2654889.","DOI":"10.1145\/2647868.2654889"},{"key":"10782_CR36","unstructured":"K He Delving Deep into Rectifiers\u202f: Surpassing Human-Level Performance on ImageNet Classification."},{"key":"10782_CR37","first-page":"2951","volume":"4","author":"J Snoek","year":"2012","unstructured":"Snoek J, Larochelle H, Adams RP (2012) Practical Bayesian optimization of machine learning algorithms. Adv Neural Inf Process Syst 4:2951\u20132959","journal-title":"Adv Neural Inf Process Syst"},{"key":"10782_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2020.e05652","author":"K Aderghal","year":"2020","unstructured":"Aderghal K, Afdel K, Benois-Pineau J, Catheline G (2020) Improving Alzheimer\u2019s stage categorization with convolutional neural network using transfer learning and different magnetic resonance imaging modalities. Heliyon. https:\/\/doi.org\/10.1016\/j.heliyon.2020.e05652","journal-title":"Heliyon"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-10782-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-022-10782-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-10782-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T07:11:28Z","timestamp":1665731488000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-022-10782-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,15]]},"references-count":38,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["10782"],"URL":"https:\/\/doi.org\/10.1007\/s11063-022-10782-0","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,15]]},"assertion":[{"value":"18 February 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 March 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no established conflicting financial interests or personal relationships that may seem to have influenced the research presented in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}