  Melanoma is the most severe type of skin cancer due to its ability to cause
metastasis. It is more common in black people, often affecting acral regions:
palms, soles, and nails. Deep neural networks have shown tremendous potential
for improving clinical care and skin cancer diagnosis. Nevertheless, prevailing
studies predominantly rely on datasets of white skin tones, neglecting to
report diagnostic outcomes for diverse patient skin tones. In this work, we
evaluate supervised and self-supervised models in skin lesion images extracted
from acral regions commonly observed in black individuals. Also, we carefully
curate a dataset containing skin lesions in acral regions and assess the
datasets concerning the Fitzpatrick scale to verify performance on black skin.
Our results expose the poor generalizability of these models, revealing their
favorable performance for lesions on white skin. Neglecting to create diverse
datasets, which necessitates the development of specialized models, is
unacceptable. Deep neural networks have great potential to improve diagnosis,
particularly for populations with limited access to dermatology. However,
including black skin lesions is necessary to ensure these populations can
access the benefits of inclusive technology.
