Channeling Fairness: Class Imbalance-Aware Skin Disease Recognition via Fair Channel Enhancement Module

Abstract

Skin disease classification presents significant challenges due to class imbalance, low inter-class variability, and high intra-class variation present in most clinical image datasets. To address these issues, we propose Fair Channel Enhancement (FCE), a novel module that improves fine-grained feature representation without requiring additional annotations nor complex architectures. FCE allocates feature channels proportionally based on class frequency, which ensures fairer representation of underrepresented classes. FCE is coupled with CutMix augmentation and label smoothing to enhance model robustness and generalization. Extensive experiments on three dermatology benchmark datasets, including SD-128, SD-198, and SD-260, demonstrate that our approach achieves up to a 7.13% accuracy improvement over baseline models and outperforms state-of-the-art methods by a significant margin. FCE also boosts the average accuracy of both low- and high-frequency classes by up to 8.60% and 10.48%, respectively. Furthermore, our method generalizes effectively to other medical image datasets, including ISIC 2018 and Hyper-Kvasir, and performs well on smaller dataset subsets. These results highlight FCE as a simple and effective solution for imbalanced classification problems.