Abstract

Mandibular segmentation is an important step in gender identification and age estimation, which aims to segment the mandible from intact and complete panoramic radiograph. One of the main drawbacks of most existing mandibular segmentation methods is that they cannot completely represent the mandible. When conducting several segmentation experiments with several methods, namely U-Net, MobileNetV2, ResNet18, ResNet50, Xception, InceptionResNet V2, MobileNetV2 turned out to be superior. However, if you only use the MobileNetV2 method, the results still need to be clarified on the coronoid and mandibular condyles. Then it is necessary to add an ensemble so that the mandibular segmentation results become more intact and complete. In contrast to the usual MobileNetV2, the mandibular segmentation results are assembled to achieve a complete and intact performance. Finally, this method experimented with 38 panoramic radiographs verified by radiologists. The experimental results show that the proposed MobileNetV2 ensemble for segmentation was superior to the usual MobileNetV2 method with a dice value of 0.9655

Original languageEnglish
Pages (from-to)546-560
Number of pages15
JournalInternational Journal of Intelligent Engineering and Systems
Volume16
Issue number2
DOIs
Publication statusPublished - 2023

Keywords

  • Ensemble segmentation
  • Mandibular segmentation
  • MobileNetV2
  • Panoramic radiography

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