Efficient Dental X-Ray Bone Loss Classification: Ensemble Learning With Fine-Tuned VIT-G/14 And Coatnet-7 For Detecting Localized Vs. Generalized Depleted Alveolar Bone

Abstract

This study applies advanced machine learning approaches, specifically deep learning models and ensemble learning, to develop an automated alveolar bone loss detection system in dental X-rays. A comprehensive dataset of dental radiographs is utilized to evaluate the proposed methodologies. The dataset comprises thousands of labeled X-ray images illustrating various stages of alveolar bone depletion. The experiment employs high-resolution dental X-ray images and sophisticated image-processing algorithms. VIT-G/14 and CoAtNet7 models are fine-tuned using the dataset to distinguish between localized and generalized depleted alveolar bone in dental X-rays. Ensemble learning is then applied to consolidate the prediction outputs from both models, consequently enhancing overall diagnostic accuracy. The implemented system showcases exceptional performance, delivering high precision and recall rates in recognizing alveolar bone loss cases. Through automation, the solution significantly reduces reliance on manual radiograph interpretation, streamlines the diagnostic procedure, and extends the reach of bone loss detection applications in dentistry. This pioneering research marks a significant stride forward in improving the early detection of periodontal disease and promoting preventive dental care.