Optimized Twin Attention-Based Deep Learning Model for Periodontal Bone Loss Detection

This paper proposes a deep-learning model for the detection of periodontal bone loss from dental radiographs. The process involves pre-processing using Cross Guided Bilateral Filtering (CGBiF), resizing and image enhancement to remove noise, followed by segmentation using a Region of Interest (ROI) model to isolate relevant periodontal structures. A novel Twin Attention-based Deep Red Convolutional Long Short Term Memory (TA_DRCLSTM) model is introduced to detect bone loss and is optimized with Enhanced Red Panda Optimization (ERPO). The model is evaluated and compared to existing approaches in terms of precision, recall, specificity, NPV and AUC-PR, achieving superior performance. The proposed model demonstrates effective and accurate detection of periodontal bone loss, providing a valuable tool for clinical applications. CGBiF improves image clarity for better detection by lowering noise and maintaining crucial features like bone structures. Resizing standardizes image dimensions, allowing for consistent model input. Image enhancement increases contrast, making periodontal structures more visible, resulting in a 93.8% recall, 81.2% precision and 95.4% AUC-PR in the study.