Abstract
This article presents a dental dataset for the improvement of research on deep learning-based detection and classification of dental diseases. The dataset is consisted of 232 panoramic dental radiographs, categorized into six major classes: healthy teeth, caries, impacted teeth, infections, fractured teeth, and broken-down crowns/roots (BDC/BDR). The images were collected from three renowned private clinics in Dhaka, Bangladesh, with the help of an experienced dental practitioner who ensured the confidentiality of patients and high-quality data acquisition using a 64-megapixel Android phone camera. To enhance the value of the dataset for machine and deep learning applications, we applied Contrast-Limited Adaptive Histogram Equalization (CLAHE) for image enhancement and augmented the data. The images were annotated using the CVAT tool and reviewed by dental experts. This benchmark dataset is publicly available and provides a valuable resource for researchers in artificial intelligence, computer science, and dental informatics to promote interdisciplinary collaboration and the development of advanced algorithms for dental disease detection.