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dc.contributor.authorAfifi, Sen_NZ
dc.contributor.authorGholamhosseini, Hen_NZ
dc.contributor.authorRoopak, Sen_NZ
dc.date.accessioned2016-02-16T03:01:36Z
dc.date.available2016-02-16T03:01:36Z
dc.date.copyright2015en_NZ
dc.identifier.citation7th Pacific Rim Symposium on Image and Video Technology held at Auckland, New Zealand, Auckland, New Zealand, 2015-11-23 to 2015-11-27en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/9537
dc.description.abstractMelanoma is the most aggressive form of skin cancer which is responsible for the majority of skin cancer related deaths. Recently, image-based Computer Aided Diagnosis (CAD) systems are being increasingly used to help skin cancer specialists in detecting melanoma lesions early, and consequently reduce mortality rates. In this paper, we implement the most compute-intensive classification stage in the CAD onto FPGA, aiming to achieve acceleration of the system for deploying as an embedded device. A hardware/software co-design approach was proposed for implementing the Support Vector Machine (SVM) classifier for classifying melanoma images online in real-time. The hybrid Zynq platform was used for implementing the proposed architecture of the SVM classifier designed using the High Level Synthesis design methodology. The implemented SVM classification system on Zynq demonstrated high performance with low resources utilization and power consumption, meeting several embedded systems constraints.
dc.publisherDepartment of Computer Science, The University of Auckland.
dc.relation.urihttp://www.psivt.org/psivt2015/program-workshopsFin.php
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in (see Citation). The original publication is available at (see Publisher's Version).
dc.subjectSVM; CAD; Melanoma; FPGA; Hardware implementation
dc.titleHardware acceleration of SVM-based classifier for melanoma imagesen_NZ
dc.typeConference Contribution
dc.rights.accessrightsOpenAccessen_NZ
aut.relation.pages12
pubs.elements-id196747


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