# A Dense CNN approach for skin lesion classification

Source: [A Dense CNN approach for skin lesion classification](<https://www.sota2.com/research/paper/68fa649b1e209131cea0585b>)

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

Data updated: 2025-10-25T21:08:02.971Z

Authors: Pierluigi Carcagn\\\`i, Andrea Cuna, Cosimo Distante

Published: 2018-07-17T13:33:41.000Z

## Abstract

This article presents a Deep CNN, based on the DenseNet architecture jointly with a highly discriminating learning methodology, in order to classify seven kinds of skin lesions: Melanoma, Melanocytic nevus, Basal cell carcinoma, Actinic keratosis / Bowen's disease, Benign keratosis, Dermatofibroma, Vascular lesion. In particular a 61 layers DenseNet, pre-trained on IMAGENET dataset, has been fine-tuned on ISIC 2018 Task 3 Challenge Dataset exploiting a Center Loss function.

## 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/1807.06416>)

[PDF](<https://arxiv.org/pdf/1807.06416.pdf>)