# The impact of patient clinical information on automated skin cancer detection

arXiv: https://arxiv.org/abs/1909.12912 · HTML: https://mlsanity.com/paper/1909.12912/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2019.

## Built on by (most cited first, 21 of 21)

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- [A Smartphone based Application for Skin Cancer Classification Using Deep Learning with Clinical Images and Lesion Information](https://mlsanity.com/paper/2104.14353/) (2021)
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- [Anisotropic Diffusion Probabilistic Model for Imbalanced Image Classification](https://mlsanity.com/paper/2409.14313/) (2024)
- [Robustness and sex differences in skin cancer detection: logistic regression vs CNNs](https://mlsanity.com/paper/2504.11415/) (2025)
- [Improving Learning of New Diseases through Knowledge-Enhanced Initialization for Federated Adapter Tuning](https://mlsanity.com/paper/2508.10299/) (2025)
- [CutisAI: Deep Learning Framework for Automated Dermatology and Cancer Screening](https://mlsanity.com/paper/2601.02562/) (2026)
- [JI-ADF: Joint-Individual Learning with Adaptive Decision Fusion for Multimodal Skin Lesion Classification](https://mlsanity.com/paper/2604.27343/) (2026)
- [An approach with Visual and Tabular Mamba to multimodal medical data using Mixed Fusion](https://mlsanity.com/paper/2606.20738/) (2026)

## Abstract (arXiv)

Skin cancer is one of the most common types of cancer around the world. For this reason, over the past years, different approaches have been proposed to assist detect it. Nonetheless, most of them are based only on dermoscopy images and do not take into account the patient clinical information. In this work, first, we present a new dataset that contains clinical images, acquired from smartphones, and patient clinical information of the skin lesions. Next, we introduce a straightforward approach to combine the clinical data and the images using different well-known deep learning models. These models are applied to the presented dataset using only the images and combining them with the patient clinical information. We present a comprehensive study to show the impact of the clinical data on the final predictions. The results obtained by combining both sets of information show a general improvement of around 7% in the balanced accuracy for all models. In addition, the statistical test indicates significant differences between the models with and without considering both data. The improvement achieved shows the potential of using patient clinical information in skin cancer detection and indicates that this piece of information is important to leverage skin cancer detection systems.