Systematic Optimization of Transfer Learning for Acne Severity Classification Using Restricted, Imbalanced and Non-Public Facial Images: An Experimental Study

Acne vulgaris is a prevalent inflammatory skin condition that requires accurate severity assessment for effective management. As a step toward more objective and reproducible severity assessment, this study developed an optimized transfer learning-based convolutional neural network (CNN) framework for facial acne severity classification using a restricted, imbalanced, non-public facial image dataset. A total of 442 frontal facial images were collected under natural lighting conditions. Acne severity was graded by a board-certified dermatologist using the Investigator’s Global Assessment (IGA) scale and categorized into three levels. The study systematically investigated model architecture selection, hyperparameter tuning, optimizer comparison, cross-validation, and class-imbalance handling through random oversampling, Synthetic Minority Over-sampling Technique (SMOTE), and Generative Adversarial Networks (GANs). The 5-fold cross-validation experiment supported the reliability of model performance across different data partitions, achieving an accuracy of 0.51. Resampling methods showed limited overall benefit, although some methods altered class-wise prediction patterns. To further examine model behavior, Gradient-weighted Class Activation Mapping (Grad-CAM) visualization was used to provide qualitative insight into the facial regions contributing to model predictions. Although the findings are limited by dataset size and diversity, the proposed framework suggests exploratory feasibility for automated acne severity assessment. Rather than serving as an immediately deployable clinical tool, this pipeline provides a preliminary baseline framework that requires further validation using larger, more diverse datasets, particularly to address subtle visual differences between acne severity classes.