Work

Bias Analysis and Data Augmentation Strategies in Skin Lesion Classification

Year: 2026

Type: article

Abstract: Early detection of skin cancer is vital for effective treatment and improving patient recovery. In recent years, a growing number of computer vision studies have been developed to aid in diagnosis, drawing significant attention from researchers. However, challenges still persist, such as data imbalance and the lack of comprehensive datasets. Additionally, limited research has focused on how variations in skin tone across different populations affect the performance of models in skin lesion classification. This study seeks to create a more effective approach to address data biases in lesion classification across diverse skin tones. We initially explored several data augmentation techniques, employing traditional feature extractors for image analysis. For classification, models such as k-Nearest Neighbor, Random Forest, and Support Vector Machine were used. This study focused on two well-known and publicly available skin lesion datasets: HAM10000 and PAD-UFES-20, both of which have significant class imbalances. Further experiments were conducted to assess potential biases, with the Individual Typology Angle (ITA) metric applied to evaluate the skin tone distribution within the datasets.

Source: Revista de Informática Teórica e Aplicada

DOI: 10.22456/2175-2745.150974

Authors: Thiago Meneses Lopes, Martin Heckmann, Marcelo Ponciano da Silva, Pedro Henrique Bugatti, Priscila Tiemi Maeda Saito

Institutions: Universidade Federal de São Carlos, Instituto Federal de Educação, Ciência e Tecnologia do Triângulo Mineiro

Language: English

Topic: Cutaneous Melanoma Detection and Management

Subfield: Oncology

Field: Medicine

Domain: Health Sciences

SDG: Reduced inequalities

Open Access status: diamond

Funders: Fundação de Amparo à Pesquisa do Estado de São Paulo, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico