An Intelligent Framework for Tooth Segmentation and Classification Through Fusion of Transfer Learning with Advanced Optimization Algorithms Using Biomedical Images

Background/Objectives: Dental health is an important component of overall well-being, and accurate tooth analysis supports several applications in dental imaging. Tooth segmentation is a crucial stage in digital dental workflows, particularly for orthodontic diagnosis, treatment planning, and the assessment of treatment-related changes such as root resorption. However, the complex anatomical structures and variations present in dental X-ray images make accurate tooth segmentation and classification challenging. To address these challenges, this study develops a Tooth Segmentation and Classification through the Fusion of Feature Extraction Models and Advanced Optimization Algorithms Using Biomedical Images (TSCFFM-AOABI) framework. Methods: The proposed framework first applies image augmentation using Blur, MedianBlur, ToGray, and Contrast-Limited Adaptive Histogram Equalization (CLAHE) to improve image variability and feature visibility. Tooth instances are subsequently segmented using YOLO11s trained with the Adam optimizer. The segmented tooth regions are then processed through DenseNet201, MobileNet, and ShuffleNetV2 to obtain complementary deep feature representations, which are fused to construct a comprehensive feature representation. The fused features are classified using a Kernel Extreme Learning Machine (KELM), whose penalty parameter C and RBF kernel parameter γ are optimized using Bacterial Colony Optimization (BCO). Results: The proposed framework is evaluated on a benchmark dental X-ray image dataset containing 598 images and 32 tooth classes. Experimental results demonstrate an accuracy of 96.17%, precision of 95.10%, recall of 99.03%, and an F1-score of 98.00%. Conclusions: The results demonstrate the effectiveness of the proposed framework for tooth segmentation and classification.