  Support Vector Machine (SVM) is a common classifier used for efficient
classification with high accuracy. SVM shows high accuracy for classifying
melanoma (skin cancer) clinical images within computer-aided diagnosis systems
used by skin cancer specialists to detect melanoma early and save lives. We aim
to develop a medical low-cost handheld device that runs a real-time embedded
SVM- based diagnosis system for use in primary care for early detection of
melanoma. In this paper, an optimized SVM classifier is implemented onto a
recent FPGA platform using the latest design methodology to be embedded into
the proposed device for realizing online efficient melanoma detection on a
single system on chip/device. The hardware implementation results demonstrate a
high classification accuracy of 97.9% and a significant acceleration factor of
26 from equivalent software implementation on an embedded processor, with 34%
of resources utilization and 2 watts for power consumption. Consequently, the
implemented system meets crucial embedded systems constraints of high
performance and low cost, resources utilization and power consumption, while
achieving high classification accuracy.

Image and Video Processing;Distributed, Parallel, and Cluster Computing