# SVM Classifier on Chip for Melanoma Detection

arXiv: https://arxiv.org/abs/2108.11957 · HTML: https://mlsanity.com/paper/2108.11957/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2021.

## Built on by (most cited first, 2 of 2)

- [Dynamic hardware system for cascade SVM classification of melanoma](https://mlsanity.com/paper/2112.05322/) (2021)
- [A system on chip for melanoma detection using FPGA-based SVM classifier](https://mlsanity.com/paper/2109.14840/) (2021)

## Abstract (arXiv)

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.