{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:25:18Z","timestamp":1742959518143,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030914141"},{"type":"electronic","value":"9783030914158"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-91415-8_11","type":"book-chapter","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T18:04:03Z","timestamp":1637172243000},"page":"118-129","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Efficient Two-Stage Fusion Network for Computer-Aided Diagnosis of Diabetic Foot"],"prefix":"10.1007","author":[{"given":"Anping","family":"Song","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongtao","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lifang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziheng","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyu","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,11,18]]},"reference":[{"unstructured":"Abdullah Mueen, Dr., Luan, S.: Early detection and prevention of diabetic foot (2013). https:\/\/www.cs.unm.edu\/~mueen\/diabeticfoot\/Proposal.pdf","key":"11_CR1"},{"issue":"21","key":"11_CR2","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, M.A., Oleiwi, S.R., Al-Shamma, O., Zhang, J.: DFU_QUTNET: diabetic foot ulcer classification using novel deep convolutional neural network. Multimedia Tools Appl. 79(21), 15655\u201315677 (2020)","journal-title":"Multimedia Tools Appl."},{"doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154\u20136162 (2018)","key":"11_CR3","DOI":"10.1109\/CVPR.2018.00644"},{"doi-asserted-by":"publisher","unstructured":"Chadwick, P.: Best practice in the management of diabetic foot ulcers and pressure ulcers on the foot. Prim. Health Care 31 (2021). https:\/\/doi.org\/10.7748\/phc.2021.e1686","key":"11_CR4","DOI":"10.7748\/phc.2021.e1686"},{"issue":"1","key":"11_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12938-017-0408-x","volume":"16","author":"L Fraiwan","year":"2017","unstructured":"Fraiwan, L., AlKhodari, M., Ninan, J., Mustafa, B., Saleh, A., Ghazal, M.: Diabetic foot ulcer mobile detection system using smart phone thermal camera: a feasibility study. Biomed. Eng. Online 16(1), 1\u201319 (2017)","journal-title":"Biomed. Eng. Online"},{"key":"11_CR6","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1109\/TETCI.2018.2866254","volume":"4","author":"M Goyal","year":"2018","unstructured":"Goyal, M., Reeves, N.D., Davison, A.K., Rajbhandari, S., Spragg, J., Yap, M.H.: DFUNet: convolutional neural networks for diabetic foot ulcer classification. IEEE Trans. Emerg. Top. Comput. Intell. 4, 728\u2013739 (2018)","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"11_CR7","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, N.D., Rajbhandari, S., Ahmad, N., Wang, C., Yap, M.H.: Recognition of ischaemia and infection in diabetic foot ulcers: dataset and techniques. Comput. Biol. Med. 117, 103616 (2020)","journal-title":"Comput. Biol. Med."},{"issue":"4","key":"11_CR8","doi-asserted-by":"publisher","first-page":"1730","DOI":"10.1109\/JBHI.2018.2868656","volume":"23","author":"M Goyal","year":"2018","unstructured":"Goyal, M., Reeves, N.D., Rajbhandari, S., Yap, M.H.: Robust methods for real-time diabetic foot ulcer detection and localization on mobile devices. IEEE J. Biomed. Health Inform. 23(4), 1730\u20131741 (2018)","journal-title":"IEEE J. Biomed. Health Inform."},{"doi-asserted-by":"crossref","unstructured":"Goyal, M., Yap, M.H., Reeves, N.D., Rajbhandari, S., Spragg, J.: Fully convolutional networks for diabetic foot ulcer segmentation. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 618\u2013623. IEEE (2017)","key":"11_CR9","DOI":"10.1109\/SMC.2017.8122675"},{"unstructured":"Han, A., et al.: Efficient refinements on YOLOv3 for real-time detection and assessment of diabetic foot Wagner grades. arXiv preprint arXiv:2006.02322 (2020)","key":"11_CR10"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","key":"11_CR11","DOI":"10.1109\/CVPR.2016.90"},{"key":"11_CR12","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3389\/fendo.2017.00025","volume":"8","author":"PU Kasbekar","year":"2017","unstructured":"Kasbekar, P.U., Goel, P., Jadhav, S.P.: A decision tree analysis of diabetic foot amputation risk in Indian patients. Front. Endocrinol. 8, 25 (2017)","journal-title":"Front. Endocrinol."},{"doi-asserted-by":"crossref","unstructured":"Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., Sun, J.: DetNet: a backbone network for object detection. arXiv preprint arXiv:1804.06215 (2018)","key":"11_CR13","DOI":"10.1007\/978-3-030-01240-3_21"},{"issue":"2","key":"11_CR14","doi-asserted-by":"publisher","first-page":"26003","DOI":"10.1117\/1.JBO.20.2.026003","volume":"20","author":"C Liu","year":"2015","unstructured":"Liu, C., Netten, J.J.V., Baal, J.G.V., Bus, S.A., Heijden, F.V.D.: Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis. J. Biomed. Opt. 20(2), 26003 (2015)","journal-title":"J. Biomed. Opt."},{"doi-asserted-by":"crossref","unstructured":"Madarasingha, K., et al.: Development of a system to profile foot temperatures of the plantar and the periphery. In: TENCON 2018\u20132018 IEEE Region 10 Conference, pp. 1928\u20131932. IEEE (2018)","key":"11_CR15","DOI":"10.1109\/TENCON.2018.8650139"},{"doi-asserted-by":"publisher","unstructured":"van Netten, J.J., van Baal, J.G., Liu, C., van Der Heijden, F., Bus, S.A.: Infrared thermal imaging for automated detection of diabetic foot complications. SAGE Publications Sage CA, Los Angeles (2013). https:\/\/doi.org\/10.1177\/193229681300700504","key":"11_CR16","DOI":"10.1177\/193229681300700504"},{"unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)","key":"11_CR17"},{"issue":"2","key":"11_CR18","doi-asserted-by":"publisher","first-page":"444","DOI":"10.2337\/dc06-2251","volume":"30","author":"LC Rogers","year":"2007","unstructured":"Rogers, L.C., Armstrong, D.G., Boulton, A.J., Freemont, A.J., Malik, R.A.: Malignant melanoma misdiagnosed as a diabetic foot ulcer. Diabetes Care 30(2), 444\u2013445 (2007)","journal-title":"Diabetes Care"},{"doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","key":"11_CR19","DOI":"10.1109\/CVPR.2016.308"},{"doi-asserted-by":"crossref","unstructured":"Vardasca, R., Magalhaes, C., Seixas, A., Carvalho, R., Mendes, J.: Diabetic foot monitoring using dynamic thermography and AI classifiers. In: Proceedings of the QIRT Asia, pp. 1\u20135 (2019)","key":"11_CR20","DOI":"10.21611\/qirt.2019.027"},{"key":"11_CR21","series-title":"IFMBE Proceedings","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1007\/978-3-319-19387-8_55","volume-title":"World Congress on Medical Physics and Biomedical Engineering","author":"L Vilcahuaman","year":"2015","unstructured":"Vilcahuaman, L., et al.: Automatic analysis of plantar foot thermal images in at-risk type II diabetes by using an infrared camera. In: Jaffray, D.A. (ed.) World Congress on Medical Physics and Biomedical Engineering. IP, vol. 51, pp. 228\u2013231. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-19387-8_55"},{"doi-asserted-by":"crossref","unstructured":"Wang, F., et al.: Residual attention network for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3156\u20133164 (2017)","key":"11_CR22","DOI":"10.1109\/CVPR.2017.683"},{"issue":"5","key":"11_CR23","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.2337\/diacare.27.5.1047","volume":"27","author":"S Wild","year":"2004","unstructured":"Wild, S., Roglic, G., Green, A., Sicree, R., King, H.: Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 27(5), 1047\u20131053 (2004)","journal-title":"Diabetes Care"},{"key":"11_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1"},{"key":"11_CR25","doi-asserted-by":"publisher","first-page":"104596","DOI":"10.1016\/j.compbiomed.2021.104596","volume":"135","author":"MH Yap","year":"2021","unstructured":"Yap, M.H., et al.: Deep learning in diabetic foot ulcers detection: a comprehensive evaluation. Comput. Biol. Med. 135, 104596 (2021)","journal-title":"Comput. Biol. Med."},{"doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929 (2016)","key":"11_CR26","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Lecture Notes in Computer Science","Bioinformatics Research and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-91415-8_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T18:06:18Z","timestamp":1637172378000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-91415-8_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030914141","9783030914158"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-91415-8_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"18 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISBRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Bioinformatics Research and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isbra2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/alan.cs.gsu.edu\/isbra21\/?q=node\/1","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"135","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.97","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.95","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}