Multi-Source Dental X-Ray Dataset Using Image-to-Image Transformation

The Teeth View X-ray Image Dataset is a collection of dental X-ray images gathered from different dental clinics. It is designed for machine learning tasks such as object detection. The dataset is organized into one main folder: the object detection dataset. The object detection folder contains 1,674 augmented images with corresponding labels in JSON format. By applying a diffusion model image to image, we have generated additional X-ray images of teeth scans to enhance the Teeth View X-ray Image Dataset to be deeper, more diverse, and beneficial for model tuning. The synthetic image contains various anatomy, density, and acquiring conditions of the tooth and thereby enhances the model's robustness for unusual dental conditions. The increased size of the dataset works to counterbalance class imbalance by having more examples of the under-represented classes and reducing model bias. Also, diffusion models demonstrate high effectiveness in reproducing noise patterns, making training more robust; improving the clarity of images through denoising processes; and enabling highly precise segmentation mask predictions to facilitate efficient boundary detection. Variables:
BDC-BDR Teeth -208
Caries Teeth - 342
Fractured Teeth -52
Healthy Teeth - 893
Impacted Teeth - 348
Inflection Teeth - 92
Folder structure: Teeth view X-ray Image dataset
|
|---Objectect Detection
-------------|
-------------|----image
|----Labels.