  Melanoma is the most dangerous form of skin cancer, which is responsible for
the majority of skin cancer-related deaths. Early diagnosis of melanoma can
significantly reduce mortality rates and treatment costs. Therefore, skin
cancer specialists are using image-based diagnostic tools for detecting
melanoma earlier. We aim to develop a handheld device featured with low cost
and high performance to enhance early detection of melanoma at the primary
healthcare. But, developing this device is very challenging due to the
complicated computations required by the embedded diagnosis system. Thus, we
aim to exploit the recent hardware technology in reconfigurable computing to
achieve a high-performance embedded system at low cost. Support vector machine
(SVM) is a common classifier that shows high accuracy for classifying melanoma
within the diagnosis system and is considered as the most compute-intensive
task in the system. In this paper, we propose a dynamic hardware system for
implementing a cascade SVM classifier on FPGA for early melanoma detection. A
multi-core architecture is proposed to implement a two-stage cascade classifier
using two classifiers with accuracies of 98% and 73%. The hardware
implementation results were optimized by using the dynamic partial
reconfiguration technology, where very low resource utilization of 1% slices
and power consumption of 1.5 W were achieved. Consequently, the implemented
dynamic hardware system meets vital embedded system constraints of high
performance and low cost, resource utilization, and power consumption, while
achieving efficient classification with high accuracy.
