DOI: 10.5281/zenodo.14571990
title: "An Annotated Clinical Image Dataset for Deep Learning-Based Classification of Oral Lesions"

description: Overview:

This intraoral dataset is collected using mobile phones and DSLR camera. It comprises 9,201 intraoral images, which are subdivided according to the presence and type of oral lesion. These include 4,405 images classified as ‘Normal,’ 2,314 as ‘Low Risk of malignant transformation,’ and 2,482 as ‘High Risk of malignant transformation'. The dataset consisted solely of original images, without any augmented photos. The dataset was comprehensively annotated by Oral Medicine specialists using LabelMe.exe in JSON (JavaScript Object Notation) format. The dataset contains the following files:



normal.zip : intraoral annotated images in the 'normal' category.

Low.zip: intraoral annotated images in the 'Low Risk of malignant transformation' category.

High.zip: intraoral annotated images in the 'High Risk of malignant transformation' category.


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