# Recent advances in deep learning applied to skin cancer detection

arXiv: https://arxiv.org/abs/1912.03280 · HTML: https://mlsanity.com/paper/1912.03280/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2019.

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

- [Early Melanoma Diagnosis with Sequential Dermoscopic Images](https://mlsanity.com/paper/2110.05976/) (2021)
- [IARS SegNet: Interpretable Attention Residual Skip connection SegNet for melanoma segmentation](https://mlsanity.com/paper/2310.20292/) (2023)

## Abstract (arXiv)

Skin cancer is a major public health problem around the world. Its early detection is very important to increase patient prognostics. However, the lack of qualified professionals and medical instruments are significant issues in this field. In this context, over the past few years, deep learning models applied to automated skin cancer detection have become a trend. In this paper, we present an overview of the recent advances reported in this field as well as a discussion about the challenges and opportunities for improvement in the current models. In addition, we also present some important aspects regarding the use of these models in smartphones and indicate future directions we believe the field will take.