EfficientNet-B0 Deep Learning Model for Accurate Classification of Intrabony Lesions in Dental Panoramic Radiographs

Objectives To develop and evaluate an EfficientNet-B0-based deep-learning model for classifying eight types of intrabony lesions on dental panoramic radiographs. Methods A dataset of 833 dental panoramic radiographs from 245 patients was collected between October 2021 and April 2023 at two dental centers. Images were classified into eight categories: Periapical Widening, Condensing Osteitis, Periapical Granuloma, Nil Control, Diffuse Lesion, Periapical Abscess, Pericoronitis, and Radicular Cyst. Data preprocessing included class-weight computation and augmentation of minority classes. The EfficientNet-B0 model was trained for 50 epochs using the Adam optimizer with learning-rate scheduling and mixed-precision training. Results The model achieved 93.04% validation accuracy, 0.9345 precision, 0.9304 recall, and 0.9295 F1-score. Performance analysis demonstrated robust classification across all lesion types, with the highest accuracy in Nil Control and Radicular Cyst identification. Conclusions The EfficientNet-B0 model demonstrates high accuracy in classifying dental intrabony lesions from panoramic radiographs, offering potential for enhanced diagnostic precision in clinical settings. Further validation across diverse clinical environments is recommended to establish a broader applicability.