# A Smartphone based Application for Skin Cancer Classification Using Deep Learning with Clinical Images and Lesion Information

arXiv: https://arxiv.org/abs/2104.14353 · HTML: https://mlsanity.com/paper/2104.14353/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 51 references that are in the map.
- First posted on arXiv 28 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones](https://mlsanity.com/paper/2007.00478/) (2020)
- [ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection](https://mlsanity.com/paper/1703.00523/) (2017)
- [Data Augmentation for Skin Lesion Analysis](https://mlsanity.com/paper/1809.01442/) (2018)
- [Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images](https://mlsanity.com/paper/1610.04662/) (2016)
- [The impact of patient clinical information on automated skin cancer detection](https://mlsanity.com/paper/1909.12912/) (2019)
- [Skin Lesion Classification Using Hybrid Deep Neural Networks](https://mlsanity.com/paper/1702.08434/) (2017)

## Built on by (most cited first, 5 of 5)

- [Diffusion models applied to skin and oral cancer classification](https://mlsanity.com/paper/2504.00026/) (2025)
- [Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-based Non-invasive Digital System](https://mlsanity.com/paper/2401.04746/) (2024)
- [Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets](https://mlsanity.com/paper/2212.06130/) (2022)
- [Skin cancer diagnosis using NIR spectroscopy data of skin lesions in vivo using machine learning algorithms](https://mlsanity.com/paper/2401.01200/) (2024)
- [Deep Learning for Dermatology: An Innovative Framework for Approaching Precise Skin Cancer Detection](https://mlsanity.com/paper/2602.17797/) (2026)

## Abstract (arXiv)

Over the last decades, the incidence of skin cancer, melanoma and non-melanoma, has increased at a continuous rate. In particular for melanoma, the deadliest type of skin cancer, early detection is important to increase patient prognosis. Recently, deep neural networks (DNNs) have become viable to deal with skin cancer detection. In this work, we present a smartphone-based application to assist on skin cancer detection. This application is based on a Convolutional Neural Network(CNN) trained on clinical images and patients demographics, both collected from smartphones. Also, as skin cancer datasets are imbalanced, we present an approach, based on the mutation operator of Differential Evolution (DE) algorithm, to balance data. In this sense, beyond provides a flexible tool to assist doctors on skin cancer screening phase, the method obtains promising results with a balanced accuracy of 85% and a recall of 96%.