This independent study aims to develop a model for segmenting proximal dental caries using a fully convolutional neural network in bitewing radio-graphs. The segmentation models were created with the explicit goal of helping dentists in segmenting dental caries in radiographs for a second opinion. To determine the most appropriate model architecture, we com-pared the performance of three fundamental segmentation models: U-Net, FPN (Feature Pyramid Network), DeepLabV3+, and XsembleNet, a combi-nation of the three preceding models. The system is evaluated in two ways. The first is to assess segmentation quality using the dice coefficient; empir-ical experiments indicate that XsembleNet has the highest dice coefficient, followed by FPN. The second evaluation is to rate models’ 12 testing bitewing radiographs segmentation. While all four models are comparable in accuracy and specificity, XsembleNet and FPN jointly achieve the high-est classification metrics score. As a result, it can be concluded that a fully convolutional neural network could be used to detect dental proximal car-ies radiographs via computer-assisted diagnosis.