Local-Global Features Fusion Network for Distinguishing Radiolucent Jaw Lesions via CBCT

Accurately distinguishing different types of jaw-bone radiation lesions (RJLs) based on cone beam computed tomography (CBCT) images is crucial for oral surgeons to choose appropriate treatment plans. Currently, only experienced radiologists can distinguish different types of RJLs from CBCT images. Therefore, it is necessary to study computing methods for accurately classifying CBCT images of different JRLs lesions. However, the lesions in CBCT images are very small, and different types of lesions exhibit high similarity on the images, posing challenges for computing methods. In this paper, we propose a Local-Global Features Fusion Network (LGFFNet) that simultaneously extracts local features and global features related to the lesions in CBCT images and fuses them. In order to distinguish different types of high similarity CBCT images, we adopted a loss function by combing focal loss and cross entropy loss to train the model, so that the model focuses more on learning the features of difficult-to-classify images while learning general distinguishing features. The experimental results on our collected dataset show that the classification performance of our method is superior to other comparative methods.