Skin Lesion Classification Using Focal Modulation Networks

The automatic classification of dermoscopic images is essential for the early diagnosis and treatment of skin cancer. However, this task remains challenging due to high visual similarity among lesion types, variations in lesion appearance across progression stages, and the presence of artifacts in the images. While deep learning-based approaches have outperformed traditional machine learning methods, many existing models are computationally intensive and offer limited interpretability. These limitations hinder their integration into clinical workflows where efficiency and transparency are critical. In this study, I propose a framework based on focal modulation networks (FMNs) for skin lesion classification. FMNs are designed to efficiently capture both local and global features, addressing the limitations of transformer-based models in processing high-resolution medical images. I evaluate four FMN variants, namely, Tiny, Small, Base, and Large, on three public datasets: ISIC 2017, ISIC 2018, and ISIC 2019. The highest classification accuracy was obtained on ISIC 2019 with 97.8%, followed by 96.4% on ISIC 2018, and 88.1% on ISIC 2017. These results match or exceed those reported in several previous studies. Additionally, FMNs offer model interpretability through modulator visualization. Overall, the proposed method provides an accurate, efficient, and transparent solution for automated skin lesion classification.