Fluorescence and reflectance-based dual-modal hyperspectral image fusion for caries diagnosis

作者：Cheng Wang, Huaxing Xu, Hongyu Tang, Ling Xin, Xueying Huang, Nuoqi Wang, Xuanbo Zhao, Xiaoling Wei, Rongjun Zhang · 发表于：Measurement · 年份：2025 · DOI：10.1016/j.measurement.2025.116701 · 被引用次数：6 · 研究领域：Dental Radiography and Imaging、Advanced Image Fusion Techniques、Infrared Thermography in Medicine

Caries is a common disease with high morbidity in the field of the oral cavity, and its accurate diagnosis is beneficial to the prevention and treatment of the disease. However, the existing methods based on single-modality optical imaging have problems with low accuracy and limited scope of application. Here, we present an approach to improve diagnostic performance by combining fluorescence and reflectance hyperspectral imaging with deep learning. We design a two-branch global and local fusion network model, GLFusionNet, using a hybrid fusion approach to decompose, extract, and fuse shared and specific features of fluorescence and reflectance hyperspectral imaging . GLFusionNet can excel caries grading diagnostic performance in a dataset containing 4,435 spectral blocks with an accuracy of 99.10 %, sensitivity of 99.15%, and specificity of 99.77%. The proposed method can potentially develop an auxiliary diagnostic tool for precisely classifying clinical caries.