Published Paper Details:

A common dental ailment called periodontitis is brought on by bacterial infection of the tooth's surrounding bone. To avoid serious consequences like tooth loss, early identification and accurate treatment are essential. Dental experts have historically diagnosed periodontal disease by manually identifying and labelling the condition, a procedure that takes a great deal of skill and involves tedious, time-consuming activities. The goal of this work is to use dental imaging datasets to automatically detect and classify periodontitis by utilising sophisticated neural network architectures. By effectively analysing photos for early-stage illness detection using deep learning techniques, the suggested method lessens the need for manual inspection. Multiple optimisation tactics inside the neural networks are compared to show how they affect detection performance. Results reveal that the suggested technique provides greater accuracy, with a 2D Convolutional Neural Network model having a detection accuracy of 96.93%. This high-performance solution highlights the promise of automated systems in strengthening diagnostic precision, efficiency, and scalability for periodontitis, thereby improving patient outcomes and streamlining clinical procedures.