Accurate and Interpretable Oral Lichen Planus Classification Using a Hybrid Framework with Attention-Guided Feature Fusion and Hybrid Optimisation

This study utilised a dataset of 1,346 clinical photographs comprising 747 OLP and 599 non-OLP cases collected from the College of Dental Medicine, Rangsit University. To address class imbalance and improve model generalisation, we applied targeted augmentation, expanding the dataset to 8,000 balanced images. The dataset was stratified into training, validation and testing subsets using an 80:10:10 split. Develop two custom convolutional neural networks (CNNs), LichenNet and Inception-LichenNet, to capture both fine-grained and high-level features. Deep features were extracted from both models and integrated using an attention-guided feature fusion strategy. To further optimise efficiency and reduce redundancy, we applied a hybrid feature selection (GWO + See Tree) optimisation. For clinical interpretability, Grad-CAM was used to generate class-discriminative visual explanations. All experiments were conducted on Kaggle GPU environments using 10-fold cross-validation to ensure reliable evaluation