Enhancing Dental X-Ray Segmentation Using Deep Learning

Dental X-ray image analysis plays a crucial role in diagnosing periodontal diseases, which significantly impact oral health. In this approach, we propose a hybrid deep learning Model for classifying dental X-ray images into periodontal and non-periodontal categories. Our method combines the strengths of VGG16 and MobileNet architectures, leveraging VGG's robust feature extraction capabilities and MobileNet's efficiency. The hybrid model is trained end-to-end, fine-tuning the convolutional base of VGG while preserving its early layers. We evaluate the model's performance on a dataset comprising periodontal and non-periodontal images, achieving competitive results in terms of accuracy and generalization. Our approach demonstrates promising potential for assisting dental professionals in early detection and classification of periodontal diseases, contributing to improved patient care and treatment planning in dental healthcare.