# Random Forests and VGG-NET: An Algorithm for the ISIC 2017 Skin Lesion Classification Challenge

Source: [Random Forests and VGG-NET: An Algorithm for the ISIC 2017 Skin Lesion Classification Challenge](<https://www.sota2.com/research/paper/68fa63301e209131ce9fd34b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:35:17.063Z

Authors: Songtao Guo, Yixin Luo, Yanzhi Song

Published: 2017-03-15T13:33:55.000Z

## Abstract

This manuscript briefly describes an algorithm developed for the ISIC 2017 Skin Lesion Classification Competition. In this task, participants are asked to complete two independent binary image classification tasks that involve three unique diagnoses of skin lesions (melanoma, nevus, and seborrheic keratosis). In the first binary classification task, participants are asked to distinguish between (a) melanoma and (b) nevus and seborrheic keratosis. In the second binary classification task, participants are asked to distinguish between (a) seborrheic keratosis and (b) nevus and melanoma. The other phases of the competition are not considered. Our proposed algorithm consists of three steps: preprocessing, classification using VGG-NET and Random Forests, and calculation of a final score.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1703.05148>)

[PDF](<https://arxiv.org/pdf/1703.05148.pdf>)