Precision Health

XD-LDL: Improving Generalization in Acne Severity Classification via Cross-Dataset Label Distribution Learning

Abstract This study introduces a novel approach to enhance acne severity classification by leveraging cross-dataset training within a Label Distribution Learning (LDL) framework. Traditional acne grading methods are often subjective and inconsistent, while automated systems struggle to generalize across diverse datasets, limiting their real-world applicability. This research addresses this challenge by integrating data from multiple sources (Acne04 and AcneSCU) to train a robust model capable of accurately classifying acne severity despite variations in image quality, lighting, and skin tone.