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.