Authors : Deepa B.; Luckeeswaran N.; Thejoram S.

Volume/Issue : Volume 11 - 2026, Issue 1 - January

Google Scholar : https://tinyurl.com/bk3h5s9v

Scribd : https://tinyurl.com/e5tc9uj9

DOI : https://doi.org/10.38124/ijisrt/26jan1601

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Diabetes mellitus is a chronic metabolic disorder that significantly affects various parts of the human body, including oral health. One of the most common oral complications linked to diabetes is periodontal disease, which leads to inflammation and bone loss around the gums. This project aims to evaluate the impact of diabetes on periodontal health using dental X-ray images and modern image classification techniques. By employing Convolutional Neural Networks (CNN), the system automatically learns and identifies subtle patterns in dental radiographs that may indicate diabetic conditions. The proposed model follows a structured workflow--collecting X-ray images, preprocessing them to remove noise and standardize dimensions, training the CNN for classification, and finally testing and predicting the diabetic condition based on gum and bone patterns. This approach eliminates the need for manual diagnosis and enhances early detection accuracy. The study demonstrates how deep learning can assist dental professionals by providing reliable, data-driven insights into the correlation between diabetes and periodontal disease, contributing to improved patient screening and preventive care.

Keywords : Diabetes Mellitus, Periodontal Health, Dental X-rays, Convolutional Neural Network (CNN), Image Classification, Deep Learning, Medical Image Analysis, Gum Disease Detection, Automated Diagnosis, Artificial Intelligence in Dentistry.

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