지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.12
- 수록면
- 509 - 517 (9page)
- DOI
- 10.4283/JMAG.2024.29.4.509
이용수
초록· 키워드
오류제보하기
The ability to accurately and efficiently identify individuals using panoramic dental radiographs (PDRs) is crucial in human identification, particularly in scenarios such as large-scale disasters and criminal investigations. However, this task poses significant challenges due to the complex anatomical structures of teeth, variations in image quality, and the need for scalable data processing methods. To address these issues, we conducted a study using a dataset of 16,383 PDR images from 2,653 individuals, applying a standardized preprocessing pipeline that included medial filter, contrast enhancement to improve input data consistency. We evaluated the performance of multiple deep learning architectures, including ResNet50, VGG16, and InceptionResNetV2. Our findings revealed that InceptionResNetV2, combined with the triplet loss function, outperformed other models. With input images resized to 299×299 for feature extraction, this model achieved remarkable results, including 99.81 % training accuracy, 99.71 % validation accuracy, and 99.69 % test accuracy. The best validation loss was 0.0121, and the test loss was 0.0134. Additionally, we developed an automated recognition software system based on this model, capable of processing each image in just 33 milliseconds on average. This system offers a fast and reliable tool for human identification, with promising applications in forensic science, disaster victim identification, and personalized medical record management.
목차
1. Introduction
2. Materials and Methods
3. Results
4. Conclusion
5. Discussion
Reference
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