AN INTELLIGENT DENTAL CYST PREDICTION FRAMEWORK USING PYRAMID SWIN VISION TRANSFORMER WITH REINFORCEMENT LEARNING - DRIVEN DIAGNOSTIC OPTIMIZATION - Dr D Karthiga Rani,; Dr C Radhamani,; Dr S Prasath, (The Bioscan, 2026)
Radiographic imaging is essential for the early and accurate prediction of dental cysts to prevent jaw destruction, tooth displacement, and delayed clinical treatment. Existing diagnostic methods rely heavily on expert interpretation and handcrafted features, resulting in inter-observer variability and poor generalization across different imaging conditions. Although recent Deep Learning (DL) approaches improve detection performance, they often fail to effectively capture multi-scale pathological patterns and lack adaptive optimization of diagnostic decisions. These limitations highlight the need for an intelligent and automated diagnostic framework. This paper proposes an intelligent dental cyst prediction framework based on a Pyramid Swin Vision Transformer with Reinforcement Learning (PSViT-RL) based diagnostic optimization.