Bayesian Hyperparameter Optimization of Lightweight CNNs for Facial Dermatological Classification

Convolutional Neural Networks (CNNs) have been widely applied for skin condition classification. However, fair comparisons across lightweight architectures are often hindered by inconsistent hyperparameter settings. This study investigates the performance of two efficient CNN architectures, EfficientNetB3 and MobileNetV3, for facial dermatological classification across seven skin condition categories. To ensure optimal and comparable performance, Bayesian hyperparameter optimization was employed, alongside data augmentation to improve generalization. Experimental results show that EfficientNetB3 achieved the highest accuracy of 91.91%, outperforming MobileNetV3 at 90.44%. Beyond model comparison, this work highlights the novelty of applying Bayesian optimization to achieve fair benchmarking of lightweight CNNs under limited dataset conditions. The best-performing model was further deployed as a mobile application using TensorFlow Lite and Flutter, demonstrating its potential for real-world dermatological support.