Denta-Hybridonet: A Hybrid CNN-Transformer Architecture for Automated Detection of Developmental Dental Anomalies in Pediatric Panoramic Radiographs

Abstract

Accurate identification of developmental dental anomalies (DDAs) in children is clinically important; however, interpreting panoramic radiographs can still vary across readers because of mixed dentition, anatomical overlap, and variable image quality. This variability may delay recognition and complicate early interventional planning. In this study, we curated a pediatric panoramic dataset of 2,001 radiographs (ages 6-14 years) spanning five categories: Dilaceration, Ectopy, Hypodontia, Taurodontism, and Healthy. All images were independently labeled by three experienced pediatric dentists. To avoid patient-level leakage, the dataset was divided into training, validation, and held-out test sets using a patient-wise split. We propose Denta-HybridoNet, a hybrid convolution-transformer architecture designed to capture both fine-grained tooth morphology and broader, arch-wide contextual patterns. Its InceptionNeXt-gMLP block supports multi-scale local representation learning, which helps the model focus on subtle morphological cues, whereas the Swin-gMLP block provides efficient global context modeling across the dental arch. In addition, a gated multilayer perceptron (gMLP) module refines the feature transformation through context-dependent modulation, strengthening diagnostically relevant signals while reducing the influence of irrelevant variation and radiographic noise. To ensure a fair comparison, we benchmarked Denta-HybridoNet against 22 recent convolutional and transformer-based models under the same training protocol and evaluation conditions. On the held-out test set, the proposed method achieved 91.15% accuracy and 91.20% F1 score, representing the best overall performance among the compared architectures. Ablation studies quantified the contributions of hybrid design and gMLP, and Grad-CAM analyses supported interpretability by highlighting clinically meaningful regions.