The impact of patient clinical information on automated skin cancer detection

TL;DR

This study demonstrates that incorporating patient clinical information with dermoscopy images significantly improves the accuracy of automated skin cancer detection systems, highlighting the importance of multi-modal data integration.

Contribution

The paper introduces a new dataset combining clinical images and patient data, and evaluates the impact of clinical information on deep learning-based skin cancer detection.

Findings

7% improvement in balanced accuracy with clinical data

Significant statistical differences between models with and without clinical info

Clinical data enhances skin cancer detection performance

Abstract

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.