SVM Classifier on Chip for Melanoma Detection

TL;DR

This paper presents an FPGA-based embedded SVM classifier for melanoma detection, achieving high accuracy, low power consumption, and significant speedup suitable for portable medical devices.

Contribution

It develops and implements an optimized SVM classifier on FPGA for real-time melanoma detection, balancing accuracy, speed, and resource efficiency.

Findings

Classification accuracy of 97.9%

26x speedup over software implementation

34% resource utilization and 2W power consumption

Abstract

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.