Tooth Segmentation and Abnormal Tooth Detection with Diagnostic Criterion in Panoramic X-Ray Images with Deep Learning Approach Derin grenme Yakla simi ile Panaromik X-I sinlari G r nt lerinde Di s B l tleme ve Tani l t ile Anormal Di s Tespiti

Arslan A., Arslan A., BİLGİN G.

33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025, İstanbul, Türkiye, 25 - 28 Haziran 2025, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/siu66497.2025.11111922
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: abnormal tooth detection, deep learning, machine learning, tooth segmentation
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

In this study, the aim is to perform tooth segmentation and abnormal tooth detection using diagnostic criteria for panoramic X-ray images. The 2023 dentex dataset was used in the research. During the research process, separate artificial intelligence models were examined for segmentation and diagnosis tasks. As a result of this study, the YOLO, U-Net, Trans-UNet, and DeepLabV3 AI models were selected, trained, and tested for the segmentation process, while customized CNN, DINOV2, ResNet, and EfficientNet models were chosen for abnormal tooth classification. At the end of the study, the YOLO model achieved the best results for segmentation with 96.45% AIoU, 94.21% AP, 95.52% AR, and 94.81% AP metrics, while the DINOV2 model obtained the best results for classification with 79.77% AA, 79% AP, 80% AR, 79% F1-score, and 64% Kappa metrics.