Exploring the Effect of Race in Automated Skin Cancer Detection

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Exploring the Effect of Race in Automated Skin Cancer Detection
Title:
Exploring the Effect of Race in Automated Skin Cancer Detection
Journal Title:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2025
DOI:
Publication Date:
31 December 2025
Citation:
Ruchir Srivastava, Ee Ping Ong, Joseph Wei En Lee, Ngiap Chuan Tan, Dawn Ai Qun Oh, and Choon Chiat Oh, Exploring the Effect of Race in Automated Skin Cancer Detection, International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2025
Abstract:
With the rising incidence of skin cancer, its automated detection can help in timely intervention, leading to a reduced burden on the healthcare system. However, majority of related research is focused on Caucasian populations which may limit its application in skin of colour. In this paper, we attempt to show that using AI models trained on predominantly Caucasian population do not perform equally well in Asian population of Fitzpatrick skin types II-III. We trained an EfficientNetB2 deep learning model on ISIC 2019 dataset (predominantly Caucasian) and evaluated it on ISIC 2019 (using cross-validation), PH2 (predominantly Caucasian) and our own dataset primarily consisting of Asians. The area under receiver operator characteristics or AUC was 0.90 on ISIC 2019 and 0.91 on the PH2 dataset, while it dropped to 0.81 for our dataset and did not improve upon fine tuning. Clinical relevance— Our results show that existing AIbased methods for skin cancer detection may not be optimized for Asian skin. This highlights the importance of specifically studying skin cancer in Asian skin.
License type:
Publisher Copyright
Funding Info:
This research was supported by the project Detection of non-melanoma skin cancers with teledermatology and machine learning (Artificial Intelligence) in the Asian skin.
Description:
© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
NA
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