Mandibular segmentation on panoramic radiographs with CNN Transfer Learning

Abstract

Gender identification and age estimation can use the mandible bone on panoramic radiographs. The identification process using the system requires a segmentation stage. Mandibular segmentation is research that has been done a lot to get an accurate object result. The purpose of this study was to segment the mandible on a panoramic radiograph using transfer learning CNN (MobileNetV2, ResNet18, ResNet50). The CNN method has been done before, so we tried to use the CNN method to produce clear and complete mandibular segmentation results on panoramic radiographs. The dataset used to train the model was taken from the Dental Hospital, Airlangga University, Surabaya. There are thousands of datasets, and based on the criteria of a radiologist, the data used are 38 images. The best result of mandibular segmentation on panoramic radiographs is the MobileNetV2 method because the highest Jaccard mean value is 0.9522.