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

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.