Identifying Suitable Deep Learning Approaches for Dental Caries Detection Using Smartphone Imaging

This study aims to identify the most suitable deep learning model for early detection of dental caries in a new database of dental diseases. The study compares the performance of residual and dense networks using standard performance metrics. Dental caries is categorized into four classes based on dental practitioner recommendations. A novel database consisting of 1064 intraoral digital RGB images from 194 patients was collected in collaboration with Bharati Vidyapeeth’s Dental College, Pune. These images were cropped to obtain a total of 987 single-tooth images, which were divided into 888 training, 45 testing, and 54 validation images. In Phase I experimentation, ResNet50V2, ResNet101V2, ResNet152, DenseNet169, and DenseNet201 were utilized. Phase II focused on ResNet50V2, DenseNet169, and DenseNet201, while Phase III concentrated on DenseNet169 and DenseNet201. For Phase I experimentation, the overall accuracy of dental caries classification ranged from 0.55 to 0.84, with DenseNet exhibiting superior performance. In Phase II, the overall accuracy varied from 0.72 to 0.78, with DenseNet achieving the highest accuracy of 0.78. Similarly, in Phase III, DenseNet201 surpassed other models with an overall accuracy of 0.93. The DenseNet201 algorithm shows promise for detecting and classifying dental caries in digital RGB images. This finding is significant for the future development of automated mobile applications based on dental photographs, which could assist dental practitioners during examinations. Additionally, it could enhance patient understanding of dental caries severity, thereby promoting dental health awareness.