Abstract:
Oral cavity is a central part of the appearance of a person and their health condition and oral
care is crucial. The consideration of good habits and the early identification of lesions are
paramount, and their diagnosis is normally performed with the help of visual examination, radiographies
and biopsies. While these methods are widely used, they present several challenges.
Early lesions are often small or resemble healthy tissue, making detection difficult. Diagnosis
is further limited by the subjectivity of visual assessment and the time-consuming nature of
radiograph interpretation. Because of these challenges, researchers are increasingly looking
at how artificial intelligence can help detect periapical lesions. Many studies have focused on
identifying teeth or dental diseases using X-ray images, but research using RGB (color) images
is rare. RGB images are easy to capture, non-invasive, and more accessible during routine dental
check-ups, making them useful for practical AI-based diagnostic tools. To fill this gap, the
objective was to use deep learning model to automatically detect dental lesions and improve
diagnostic accuracy. In this approach, we evaluate and compare different convolutional neural
network (CNN) architectures for identifying three major dental lesions namely, Gingivitis, Calculus,
and Hypodontia from 4000 optical color images captured in front of the mouth. After
pre-processing and extracting features, the dataset was trained with three pre-trained architectures:
EfficientNetB0, DenseNet121, and ResNet50. The findings indicate obvious variations
in performance, and DenseNet121 has always got the maximum accuracy of 86.91% and higher
precision, recall values, f-measure values compared to other models. The future dental industry
may benefit from this research as it will be easier to detect issues early and offer cheap equipment
to improve oral health. The research compares the CNN performance on dental lesion
classification and prepares the way to predict the severity and medical application in the future.
