Skin Type Diversity in Skin Lesion Datasets: A Review.

ABSTRACT:

Purpose of review

Skin type diversity in image datasets refers to the representation of various skin types. This diversity allows for the verification of comparable performance of a trained model across different skin types. A widespread problem in datasets involving human skin is the lack of verifiable diversity in skin types, making it difficult to evaluate whether the performance of the trained models generalizes across different skin types. For example, the diversity issues in skin lesion datasets, which are used to train deep learning-based models, often result in lower accuracy for darker skin types that are typically under-represented in these datasets. Under-representation in datasets results in lower performance in deep learning models for under-represented skin types.

SUBMITTER: Alipour N 

PROVIDER: S-EPMC11343783 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature