{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T19:07:51Z","timestamp":1783192071313,"version":"3.54.6"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2018,8,2]],"date-time":"2018-08-02T00:00:00Z","timestamp":1533168000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2020,3]]},"DOI":"10.1007\/s00521-018-3656-1","type":"journal-article","created":{"date-parts":[[2018,8,2]],"date-time":"2018-08-02T08:07:59Z","timestamp":1533197279000},"page":"1777-1788","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Dynamic hardware system for cascade SVM classification of melanoma"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2383-4721","authenticated-orcid":false,"given":"Shereen","family":"Afifi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamid","family":"GholamHosseini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roopak","family":"Sinha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,8,2]]},"reference":[{"key":"3656_CR1","first-page":"77","volume":"2008","author":"T Saegusa","year":"2008","unstructured":"Saegusa T, Maruyama T, Yamaguchi Y (2008) How fast is an FPGA in image processing? Int Conf Field Program Log Appl FPL 2008:77\u201382","journal-title":"Int Conf Field Program Log Appl FPL"},{"key":"3656_CR2","unstructured":"Hussain HM, Benkrid K, Seker H (2013) The role of FPGAs as high performance computing solution to bioinformatics and computational biology data. In: AIHLS2013, p 102"},{"key":"3656_CR3","unstructured":"Asano S, Maruyama T, Yamaguchi Y (2009) Performance comparison of FPGA, GPU and CPU in image processing. In: International conference on field programmable logic and applications, 2009. FPL 2009, pp 126\u2013131"},{"key":"3656_CR4","doi-asserted-by":"crossref","unstructured":"Pietron M, Wielgosz M, Zurek D, Jamro E, Wiatr K (2013) Comparison of GPU And FPGA implementation of SVM algorithm for fast image segmentation. In: Architecture of computing systems\u2013ARCS 2013, pp 292\u2013302. Springer","DOI":"10.1007\/978-3-642-36424-2_25"},{"key":"3656_CR5","unstructured":"Fykse E (2013) Performance comparison of GPU, DSP and FPGA implementations of image processing and computer vision algorithms in embedded systems. M.Sc. thesis, Department of Electronics and Telecommunications, Norwegian University of Science and Technology"},{"key":"3656_CR6","first-page":"1898","volume":"1","author":"TN Sasamal","year":"2011","unstructured":"Sasamal TN, Prasad R (2011) Module based and difference based implementation of partial reconfiguration on FPGA: a review. Int J Eng Res Appl (IJERA) 1:1898\u20131903","journal-title":"Int J Eng Res Appl (IJERA)"},{"key":"3656_CR7","doi-asserted-by":"crossref","unstructured":"Sabouri P, GholamHosseini H, Larsson T, Collins J (2014) A cascade classifier for diagnosis of melanoma in clinical images. In: 36th annual international conference of the ieee engineering in medicine and biology society (EMBC), pp 6748\u20136751","DOI":"10.1109\/EMBC.2014.6945177"},{"key":"3656_CR8","first-page":"733","volume":"2","author":"SM Afifi","year":"2015","unstructured":"Afifi SM, GholamHosseini H, Sinha R (2015) Hardware implementations of SVM on FPGA: a state-of-the-art review of current practice. Int J Innov Sci Eng Technol (IJISET) 2:733\u2013752","journal-title":"Int J Innov Sci Eng Technol (IJISET)"},{"key":"3656_CR9","doi-asserted-by":"crossref","unstructured":"Kyrkou C, Theocharides T, Bouganis C.-S (2013) An embedded hardware-efficient architecture for real-time cascade support vector machine classification. In: 2013 International conference on embedded computer systems: architectures, modeling, and simulation (SAMOS XIII), pp 129\u2013136","DOI":"10.1109\/SAMOS.2013.6621115"},{"key":"3656_CR10","doi-asserted-by":"crossref","unstructured":"Afifi S, GholamHosseini H, Sinha R (2017) SVM classifier on chip for melanoma detection. In: The 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC\u201917)","DOI":"10.1109\/EMBC.2017.8036814"},{"key":"3656_CR11","doi-asserted-by":"crossref","unstructured":"Papadonikolakis M, Bouganis C.-S (2010) A novel FPGA-based SVM classifier. In: International conference on field-programmable technology (FPT), pp 283\u2013286","DOI":"10.1109\/FPT.2010.5681485"},{"key":"3656_CR12","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1109\/TNNLS.2012.2196446","volume":"23","author":"M Papadonikolakis","year":"2012","unstructured":"Papadonikolakis M, Bouganis C (2012) Novel cascade FPGA accelerator for support vector machines classification. IEEE Trans Neural Netw Learn Syst 23:1040\u20131052","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3656_CR13","doi-asserted-by":"crossref","unstructured":"Kyrkou C, Bouganis C.-S, Theocharides T, Polycarpou MM (2015) Embedded hardware-efficient real-time classification with cascade support vector machines. In: IEEE transactions on neural networks and learning systems","DOI":"10.1109\/TNNLS.2015.2428738"},{"key":"3656_CR14","doi-asserted-by":"crossref","unstructured":"Kyrkou C, Theocharides T, Bouganis CS (2013) A hardware-efficient architecture for embedded real-time cascaded support vector machines classification. In: proceedings of the 23rd ACM international conference on Great lakes symposium on VLSI, pp 341\u2013342","DOI":"10.1145\/2483028.2483133"},{"issue":"6","key":"3656_CR15","doi-asserted-by":"publisher","first-page":"1220","DOI":"10.1007\/s10766-017-0514-1","volume":"46","author":"Christos Kyrkou","year":"2017","unstructured":"Kyrkou C, Theocharides T, Bouganis C.-S, Polycarpou M (2017) Boosting the hardware-efficiency of cascade support vector machines for embedded classification applications. In: International Journal of parallel programming, pp 1\u201327","journal-title":"International Journal of Parallel Programming"},{"key":"3656_CR16","doi-asserted-by":"crossref","unstructured":"Hussain HM, Benkrid K, Seker H (2013) Reconfiguration-based implementation of SVM classifier on FPGA for classifying microarray data. In: 2013 35th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 3058\u20133061","DOI":"10.1109\/EMBC.2013.6610186"},{"key":"3656_CR17","doi-asserted-by":"publisher","first-page":"3371","DOI":"10.3906\/elk-1402-18","volume":"24","author":"H Hussain","year":"2016","unstructured":"Hussain H, Benkrid K, \u015eeker H (2016) Novel dynamic partial reconfiguration implementations of the support vector machine classifier on FPGA. Turk J Electric Eng Comput Sci 24:3371\u20133387","journal-title":"Turk J Electric Eng Comput Sci"},{"key":"3656_CR18","doi-asserted-by":"crossref","unstructured":"Hussain HM, Benkrid K, Seker H (2015) Dynamic partial reconfiguration implementation of the SVM\/KNN multi-classifier on FPGA for bioinformatics application. In: 37th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 7667\u20137670","DOI":"10.1109\/EMBC.2015.7320168"},{"key":"3656_CR19","unstructured":"Vivado High-Level Synthesis. Available: \nhttp:\/\/www.xilinx.com\/products\/design-tools\/vivado\/integration\/esl-design.html"},{"key":"3656_CR20","unstructured":"Zynq-7000 All Programmable SoC. Available: \nhttp:\/\/www.xilinx.com\/products\/silicon-devices\/soc\/zynq-7000.html"},{"key":"3656_CR21","unstructured":"Vivado Design Suite. Available: \nhttp:\/\/www.xilinx.com\/products\/design-tools\/vivado.html"},{"key":"3656_CR22","first-page":"346","volume":"2014","author":"M Ning","year":"2014","unstructured":"Ning M, Shaojun W, Yeyong P, Yu P (2014) Implementation of LS-SVM with HLS on Zynq. Int Conf Field Program Technol (FPT) 2014:346\u2013349","journal-title":"Int Conf Field Program Technol (FPT)"},{"key":"3656_CR23","doi-asserted-by":"crossref","unstructured":"Tsoutsouras V, Koliogeorgi K, Xydis S, Soudris D (2017) An exploration framework for efficient high-level synthesis of support vector machines: case study on ECG arrhythmia detection for Xilinx Zynq SoC. J Signal Proces Syst 1\u201321","DOI":"10.1007\/s11265-017-1230-1"},{"key":"3656_CR24","unstructured":"Vivado Design Suite User guide, High-Level Synthesis. Available: \nhttp:\/\/www.xilinx.com\/support\/documentation\/sw_manuals\/xilinx2014_1\/ug902-vivado-high-level-synthesis.pdf"},{"key":"3656_CR25","unstructured":"Joachims T (1999) Making large-scale SVM learning practical. In: Advances Kernel methods: support vector learning, B. Sch\u00f6lkopf and C. Burges and A. Smola (ed.): MIT Press"},{"key":"3656_CR26","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/978-3-319-30285-0_19","volume-title":"Image and Video Technology \u2013 PSIVT 2015 Workshops","author":"Shereen Afifi","year":"2016","unstructured":"Afifi S, GholamHosseini H, Sinha R (2016) Hardware Acceleration of SVM-based classifier for melanoma images. In: Huang F, Sugimoto A (eds) Image and video technology\u2014PSIVT 2015 workshops: RV 2015, GPID 2013, VG 2015, EO4AS 2015, MCBMIIA 2015, and VSWS 2015, Auckland, New Zealand, November 23-27, 2015. Revised Selected Papers. Springer International Publishing. Cham, pp 235\u2013245"},{"key":"3656_CR27","doi-asserted-by":"crossref","unstructured":"Afifi S, GholamHosseini H, Sinha R (2016) A low-cost FPGA-based SVM classifier for melanoma detection. In: IEEE-EMBS conferences on biomedical engineering and Sciences","DOI":"10.1109\/IECBES.2016.7843526"},{"key":"3656_CR28","doi-asserted-by":"crossref","unstructured":"Ago Y, Nakano K, Ito Y (2013) A classification processor for a support vector machine with embedded DSP slices and block RAMs in the FPGA. In: 2013 IEEE 7th international symposium on embedded multicore SoCs (MCSoC), pp 91\u201396","DOI":"10.1109\/MCSoC.2013.30"},{"key":"3656_CR29","unstructured":"Berberich M, Doll K (2014) Highly flexible FPGA-architecture of a support vector machine. In: MPC-Workshop vol 45, pp 25\u201332"},{"key":"3656_CR30","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1109\/TC.2011.113","volume":"61","author":"C Kyrkou","year":"2012","unstructured":"Kyrkou C, Theocharides T (2012) A parallel hardware architecture for real-time object detection with support vector machines. IEEE Trans Comput 61:831\u2013842","journal-title":"IEEE Trans Comput"},{"key":"3656_CR31","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1109\/LES.2009.2034709","volume":"1","author":"C Kyrkou","year":"2009","unstructured":"Kyrkou C, Theocharides T (2009) SCoPE: towards a systolic array for SVM object detection. IEEE Embed Syst Lett 1:46\u201349","journal-title":"IEEE Embed Syst Lett"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3656-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-018-3656-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3656-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,2,29]],"date-time":"2020-02-29T03:03:43Z","timestamp":1582945423000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-018-3656-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,2]]},"references-count":31,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2020,3]]}},"alternative-id":["3656"],"URL":"https:\/\/doi.org\/10.1007\/s00521-018-3656-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,2]]},"assertion":[{"value":"30 December 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 August 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}