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A system on chip for melanoma detection using FPGA-based SVM classifier

journal contribution
posted on 2024-04-12, 06:22 authored by S Afifi, H GholamHosseini, R Sinha
Support Vector Machine (SVM) is a robust machine learning model that shows high accuracy with different classification problems, and is widely used for various embedded applications. However, implementation of embedded SVM classifiers is challenging, due to the inherent complicated computations required. This motivates implementing the SVM on hardware platforms for achieving high performance computing at low cost and power consumption. Melanoma is the most aggressive form of skin cancer that increases the mortality rate. We aim to develop an optimized embedded SVM classifier dedicated for a low-cost handheld device for early detection of melanoma at the primary healthcare. In this paper, we propose a hardware/software co-design for implementing the SVM classifier onto FPGA to realize melanoma detection on a chip. The implemented SVM on a recent hybrid FPGA (Zynq) platform utilizing the modern UltraFast High-Level Synthesis design methodology achieves efficient melanoma classification on chip. The hardware implementation results demonstrate classification accuracy of 97.9%, and a significant hardware acceleration rate of 21 with only 3% resources utilization and 1.69 W for power consumption. These results show that the implemented system on chip meets crucial embedded system constraints of high performance and low resources utilization, power consumption, and cost, while achieving efficient classification with high classification accuracy.

History

Journal

Microprocessors and Microsystems

Volume

65

Pagination

57-68

Location

Amsterdam, The Netherlands

ISSN

0141-9331

eISSN

1872-9436

Language

English

Publication classification

C1.1 Refereed article in a scholarly journal

Publisher

ELSEVIER SCIENCE BV