This study aimed to determine a fundamental method for the automated detection and treatment of dental and orthodontic problems. Manual intervention is inefficient and prone to human error in detecting anomalies. Deep learning was used to identify a solution to this problem. We proposed leveraging incremental learning approaches using Mask RCNN as backbone networks on small datasets to construct a more accurate model from automatically labeled data. The knowledge acquired at one stage of education is carried over to the subsequent stage. By incorporating newly annotated data, transfer learning improved the model's performance. Despite the data scarcity issues inherent in radiograph image collection, the findings for filling and tooth segmentation tasks were encouraging and adequate. We compared our results to prior research to optimize the performance of our proposed method.