The impact of patient clinical information on automated skin cancer detection

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.

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• A Smartphone based Application for Skin Cancer Classification Using Deep Learning with Clinical Images and Lesion Information
• Recent advances in deep learning applied to skin cancer detection
• Artificial Intelligence-Based Image Classification for Diagnosis of Skin Cancer: Challenges and Opportunities
• Fusing Medical Image Features and Clinical Features with Deep Learning for Computer-Aided Diagnosis
• Robustness and sex differences in skin cancer detection: logistic regression vs CNNs
• Skin-Health Monitoring system using a Wireless Body Area Network