• Sengunthar Santoshi Ramesh
• Dr. Sankarsan Panda
• Dr. S. Sreenath Kashyap

Dental image analysis plays a crucial role in detecting and diagnosing oral diseases such as dental caries, periodontal infections, and bone loss. With the evolution of AI and ML, automated dental diagnostics have become more accurate and efficient, leveraging large-scale datasets such as PRD2022 and DXR-1, automated dental diagnostics have become more accurate and efficient, leveraging large-scale dental image datasets such as PRD-2022 and DXR-1. This review paper discusses the existing literature, techniques, and challenges related to dental image classification using two fundamental ML algorithms, Naive Bayes and Logistic Regression. The paper compares their performance, discusses data preprocessing and feature extraction methods, and highlights future possibilities of hybrid and ensemble-based approaches for improving diagnostic precision. A dataset of 2,000 dental panoramic X-ray images was analyzed and collected from the PRD-2022 and DXR-1 datasets using texture, shape, and intensity features. Naive Bayes achieved 82% accuracy, while Logistic Regression reached 85% accuracy.

Dental Image Analysis, Naive Bayes, Logistic Regression, Machine Learning, Disease Prediction.

"A Review on Dental Image Analysis Techniques Using Naive Bayes and Logistic Regression Classifiers", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 12, page no.a632-a639, December-2025, Available :http://www.jetir.org/papers/JETIR2512084.pdf

"A Review on Dental Image Analysis Techniques Using Naive Bayes and Logistic Regression Classifiers", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 12, page no. ppa632-a639, December-2025, Available at : http://www.jetir.org/papers/JETIR2512084.pdf