Diversity Issues in Skin Lesion Datasets

Abstract

Melanoma is one of the most threatening skin cancers in the world, which may spread to other parts of the body if it has not been detected at an early stage. Thus, researchers have put extra efforts into using computer-aided methods to help dermatologists to recognise this kind of cancer. There are many methods for solving this issue, many based on deep learning models. In order to train these models and have high accuracy, datasets which are large enough to cover gender, race, and skin type diversity are required. Although there is a large body of data on melanoma and skin lesions, most do not cover a broad diversity of skin types, which can affect the accuracy of models trained on them. To understand the issue, first the diversity of each database must be assessed and then, based on the existing shortcomings, such as minority skin types, a suitable method must be developed to solve any diversity issues. This article summarizes the problem of the lack of diversity in gender, race and skin type in skin lesion datasets and takes a brief look at potential solutions to this problem, especially the lesser discussed colour-based methods.