ENSEMBLE OF CONVOLUTIONAL NEURAL NETWORKS FOR TOOTH SEGMENTATION ON PANORAMIC X-RAY IMAGES
DOI:
https://doi.org/10.15588/1607-3274-2026-2-6Keywords:
deep learning, convolutional neural networks, tooth segmentation, semantic segmentation, U-Net, ensembleAbstract
Context. Semantic segmentation of teeth on panoramic X-ray images is an important task in dental diagnostics, as it allows for the automation of the diagnosis of dental diseases. However, panoramic X-rays have a complex structure, which complicates the task of segmentation. The use of convolutional neural networks shows high potential in solving this problem. In this context, it is relevant to study the best models and combine them into an ensemble in order to improve the quality of segmentation.
Objective. The aim of this work is to study the effectiveness of various convolutional neural network architectures in the task of
semantic tooth segmentation on panoramic X-ray images and to develop an ensemble approach to improve the quality of the results.
Method. Various architectures of convolutional neural networks are used: U-Net, Attention U-Net, Residual U-Net, Residual Attention U-Net, R2 U-Net, U-Net++, U-Net 3+, USE-Net, Dense U-Net, and DeepLabV3+ with a pre-trained ResNet-101 backbone on the ImageNet dataset. An ensemble approach based on the best models is proposed, where the final segmentation mask is determined by majority voting. The models were trained on a preprocessed dataset of panoramic X-ray images with the application of augmentation techniques. The performance of the models was evaluated using the IoU, Dice, and Accuracy metrics.
Results. Various neural network models were investigated, and the best ones were combined into an ensemble. The conducted experiments confirmed that the ensemble approach improves segmentation accuracy compared to individual models. The best result was achieved by the ensemble combining the Dense U-Net, Attention U-Net, and U-Net 3+ architectures.
Conclusions. The proposed ensemble approach demonstrated high efficiency in the task of semantic tooth segmentation on panoramic X-ray images, outperforming the results of individual models. The scientific novelty of the study lies in the application of an ensemble approach that combines various architectures of convolutional neural networks for semantic tooth segmentation on X-ray images. The practical significance of the work is in the potential use of the developed approach for building automated diagnostic systems in dentistry. The obtained results can be applied to further automate the analysis of X-ray images and contribute to the development of intelligent medical systems.
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