RADIOGRAPH IMAGE-BASED CLASSIFICATION OF PERIODONTAL DISEASE USING DEEP LEARNING TECHNIQUES: Deep learning

Abstract

Dental radiography is useful for clinical diagnosis, treatment, and quality assessment. Much effort has gone into developing digitalized dental X-ray image analysis systems to improve clinical quality. The preprocessing of dataset, procedures, and result evaluation of dental treatment performed using periapical X-ray images taken before and after the operation. I propose a tool pipeline for automated clinical quality evaluation to assist dentists in making clinical decisions. I use Deep Learning technique to detect the disease from the X-Ray images. The dataset contains 525 dental X-Ray images. X-Ray images are labelled as Normal and Diseased by designated dental experts. This research explores the application of deep learning models for dental disease detection, focusing on two advanced architectures: ResNet101 and ResNet152. The study involves training and evaluating these models on a curated dental dataset to assess their performance in classifying dental images. ResNet101, configured with a batch size of 256 and a learning rate of 0.001, achieved a training loss of 0.002 and a test loss of 0.015. The model demonstrated perfect training accuracy of 100% and a commendable test accuracy of 98.35%, indicating strong learning and generalization capabilities. In comparison, ResNet152, with a batch size of 64 and a higher learning rate of 0.01, exhibited a training loss of 0.02 and an exceptionally low-test loss of 0.001. The model achieved a training accuracy of 100% and a test accuracy of 99.15%, showcasing superior generalization to unseen data. The results highlight the effectiveness of both models in dental disease detection, with ResNet152 showing a marginally better performance on test data.