A Dataset of apical periodontitis lesions in panoramic radiographs for deep-learning-based classification and detection

Abstract

Deep learning has been studied in recent years to identify periapical lesions- a significant indicator of periapical periodontitis in radiographs. An accurate dataset is essential for constructing an efficient learning model for detecting periapical lesions. In order to achieve this goal, we gathered and created a database of panoramic radiographs containing periapical lesions from the High-quality Dental Treatment Centre, School of Dentistry, Hanoi Medical University, between January 2016 and March 2021. Out of 16,519 radiographs, three experienced dentists identified 3,926 images of periapical lesions and annotated those lesions based on the Periapical Lesions Classification. By applying well-known data processing techniques (e.g. scaling, mirroring, and flipping), the amount of data is increased to 17,004 images through generating additional images for machine learning. The dataset has three folders: one for the original photos, one for the post-augmentation images, and the rest for the annotation of periapical lesions. The information could assist researchers in developing a predictive machine model for detecting periapical lesions in radiographs.

Keywords

• Apical periodontitis
• Machine learning
• Computer vision