A Deep Learning Framework for Automated Detection and Classification of Four Common Dental Diseases Using Dental Radiographs
Description
Dataset Description: This dataset consists of Orthopantomogram (OPG) dental X-ray images collected from the Global Orthopedic General Hospital & Diagnostic Center in Bangladesh. It is designed for object detection, disease classification, image analysis, and segmentation. The dataset is organized into two main folders: Object Detection Dataset and Classification Dataset.The Object Detection Dataset folder contains 4000 original and 10,000 augmented images with labeled annotations. The Classification Dataset folder consists of separate files for each dental condition class.Images are stored in JPG format, while labels are in JSON format. The dataset is divided into training (70%), validation (20%), and testing (10%) sets. Dataset collection: • Source: Global Orthopedic General Hospital and Diagnostic Center. • Capture Method: Hard drive. • Anonymization : All information was carefully removed to protect privacy and confidentiality. • Informed Consent: Consent was knowingly given by each patient in compliance with dental ethics. Variables: Cavities:2500 Damage/Broken :2500 Infection: 2500 Wisdom teeth: 2500