DENTAL CARIES SEVERITY DETECTION WITH A COMBINATION OF INTRAORAL IMAGES AND BITEWING RADIOGRAPHS

Dental caries is a multifactorial oral disease caused by plaque due to bacterial sugar fermentation. Quite a number of dentists have misdiagnosed caries due to the subjective nature of visual examination and radiograph in early-stage lesions. Thus, research on the implementation of deep learning technology is expected to improve the accuracy of diagnosis. However, caries detection with deep learning has accuracy problems. This problem makes researchers interested in developing a deep learning method that combines Faster R-CNN algorithm and texture feature extraction to more accurately detect carious teeth from bitewing radiography datasets and intraoral images. The overall performance of the model to detect the radiographic class was slightly better than the intraoral class. Overall, the classification accuracy of the model was 88.95% which is better than previous research that only used one or the other type of images. GLCM (Gray-Level Co-Occurrence Matrix) is effective in detecting contrast areas, but it still cannot specifically distinguish normal anatomical contrast from caries. The Faster R-CNN model learned well and was able to differentiate between each caries type and was successfully integrated with the GLCM matrix for radiographic image pre-processing to facilitate caries detection. This approach could have the potential of assisting dental professionals in reducing diagnostic errors and increasing patient care.