# Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks

Source: [Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks](<https://www.sota2.com/research/paper/68fa63341e209131ce9fd625>)

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

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

Authors: Terrance DeVries, Dhanesh Ramachandram

Published: 2017-03-04T06:32:15.000Z

## Abstract

We present a deep learning approach to the ISIC 2017 Skin Lesion Classification Challenge using a multi-scale convolutional neural network. Our approach utilizes an Inception-v3 network pre-trained on the ImageNet dataset, which is fine-tuned for skin lesion classification using two different scales of input images.

## 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/1703.01402>)

[PDF](<https://arxiv.org/pdf/1703.01402.pdf>)