Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2025, Cilt: 35 Sayı: 2, 137 - 141, 20.04.2025

Berrin Çelik Fikret Ulus Ertuğrul Furkan Savaştaer Mehmet Zahid Genç Mahmut Emin Çelik

https://doi.org/10.17567/currresdentsci.1677708

Öz

Kaynakça

  • 1. Im J, Kim JY, Yu HS, et al. Accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning. Sci Rep. 2022;12(1):9429. doi:10.1038/s41598-022-13595-2
  • 2. Liu M, Wang S, Chen H, Liu Y. A pilot study of a deep learning approach to detect marginal bone loss around implants. BMC Oral Health. 2022;22(1):11. doi:10.1186/s12903-021-02035-8
  • 3. Choi HR, Siadari TS, Kim JE, et al. Automatic detection of teeth and dental treatment patterns on dental panoramic radiographs using deep neural networks. Forensic Sci Res. 2022;7(3):456-466. doi:10.1080/20961790.2022.2034714
  • 4. Kaya M, Bilge HS. Classification of pancreas tumor dataset using adaptive weighted k nearest neighbor algorithm. 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings. 2014:253-257. doi:10.1109/inista.2014.6873626
  • 5. Kaya M, Bilge HS. Classification of Parkinson speech data by metric learning. 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). 2017:1-5. doi:10.1109/idap.2017.8090285
  • 6. Chollet F. Xception: Deep Learning with Depthwise Separable Convolutions. arXiv.org. 2016. https://arxiv.org/abs/1610.02357
  • 7. Szegedy C, Liu W, Jia Y, et al. Going Deeper with Convolutions. arXiv.org. 2014. https://arxiv.org/abs/1409.4842
  • 8. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. arXiv.org. 2015. https://arxiv.org/abs/1512.03385
  • 9. Zhang X, Zhou X, Lin M, Sun J. ShuffleNet: an extremely efficient convolutional neural network for mobile devices. arXiv.org. 2017. https://arxiv.org/abs/1707.01083
  • 10. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. MobileNetV2: Inverted residuals and linear bottlenecks. arXiv.org. 2018. https://arxiv.org/abs/1801.04381
  • 11. Xie S, Girshick R, Dollár P, Tu Z, He K. Aggregated residual transformations for deep neural networks. arXiv.org. 2016. https://doi.org/10.48550/arXiv.1611.05431
  • 12. Mohammad-Rahimi H, Motamedian SR, Rohban MH, et al. Deep learning for caries detection: A systematic review. J Dent. 2022;122:104115. doi:10.1016/j.jdent.2022.104115
  • 13. Çelik B, Çelik ME. Automated detection of dental restorations using deep learning on panoramic radiographs. Dentomaxillofac Radiol. 2022;51(8):20220244. doi:10.1259/dmfr.20220244
  • 14. Chang HJ, Lee SJ, Yong TH, et al. Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis. Sci Rep. 2020;10(1):7531. doi:10.1038/s41598-020-64509-z
  • 15. Çelik B, Çelik ME. Root Dilaceration Using Deep Learning: A Diagnostic Approach. Appl. Sci. 2023;13(14):8260.
  • 16. Mohammad-Rahimi H, Rokhshad R, Bencharit S, Krois J, Schwendicke F. Deep learning: A primer for dentists and dental researchers. J Dent. 2023;130:104430.
  • 17. Celik ME. Deep learning based detection tool for impacted mandibular third molar teeth. Diagnostics. 2022;12(4):942. doi:10.3390/diagnostics12040942
  • 18. Umer F, Habib S, Adnan N. Application of deep learning in teeth identification tasks on panoramic radiographs. Dentomaxillofac Radiol. 2022;51(5): 20210504. doi:10.1259/dmfr.20210504
  • 19. Çelik B, Savaştaer EF, Kaya HI, Çelik ME. The role of deep learning for periapical lesion detection on panoramic radiographs. Dentomaxillofac Radiol. 2023;52(8):20230118. doi:10.1259/dmfr.20230118
  • 20. Mureșanu S, Almășan O, Hedeșiu M, Dioșan L, Dinu C, Jacobs R. Artificial intelligence models for clinical usage in dentistry with a focus on dentomaxillofacial CBCT: a systematic review. Oral Radiol. 2023;39(1):18-40. doi:10.1007/s11282-022-00660-9
  • 21. Li Z, Wang S, Fan R, Cao G, Zhang Y, Guo T. Teeth category classification via seven‐layer deep convolutional neural network with max pooling and global average pooling. Int J Imaging Syst Technol. 2019;29(4):577-583. doi:10.1002/ima.22337
  • 22. Sukegawa S, Matsuyama T, Tanaka F, et al. Evaluation of multi-task learning in deep learning-based positioning classification of mandibular third molars. Sci Rep. 2022;12(1):684. doi:10.1038/s41598-021-04603-y
  • 23. Estai M, Tennant M, Gebauer D, et al. Deep learning for automated detection and numbering of permanent teeth on panoramic images. Dentomaxillofac Radiol. 2022;51(2):20210296. doi:10.1259/dmfr.20210296
  • 24. Krois J, Schneider L, Schwendicke F. Impact of Image Context on Deep Learning for Classification of Teeth on Radiographs. J Clin Med. 2021;10(8):1635. doi:10.3390/jcm10081635
  • 25. Panetta K, Rajendran R, Ramesh A, Rao S, Agaian S. Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems. IEEE J Biomed Health Inform. 2022;26(4):1650-1659. doi:10.1109/JBHI.2021.3117575

Image Processing for Tooth Type Classification using Deep Learning

Yıl 2025, Cilt: 35 Sayı: 2, 137 - 141, 20.04.2025

Berrin Çelik Fikret Ulus Ertuğrul Furkan Savaştaer Mehmet Zahid Genç Mahmut Emin Çelik

https://doi.org/10.17567/currresdentsci.1677708

Öz

Objective: Tooth classification is a crucial aspect of dentistry, influencing effective diagnosis, treatment planning, and overall oral health. However, the subjectivity and variations in human judgment, coupled with the complexity of dental conditions, have led to disparities in tooth classification, particularly in cases involving multiple dentists' opinions, varying clinical expertise, and differing dental standards. Recent advances in technology and artificial intelligence have created new opportunities for innovative solutions in tooth classification. This paper aims to investigate the effect of image processing techniques on classification performance of teeth using deep learning. 4 classes - Incisor, Canine, Premolar, Molar- from panoramic radiographs are prepared.
Methods: The state-of-the-art 6 deep learning classification models -Xception, GoogleNet, ResNet18, ShuffleNet, MobileNetV2, ResNext50- was implemented with transfer learning for model efficiency. Two models with the highest and lowest performance were chosen for further analysis related to image processing. 10 different image processing techniques (Gaussian Noise, Gaussian Blur, Wavelet Transform, Sharpness, Contrast Enhancement, Color Correction, Elastic Transform, Random Erasing, Local Binary Patterns, Local Max Min) were applied to these two models.
Results: The Xception provided the highest accuracy of 90.25% while ResNet18 yielded the lowest accuracy of 74.86%. Additionally, findings indicated that certain image processing techniques can improve classification performance.
Conclusion: The present work shows that image processing can enhance automated artificial intelligence-based solutions for more robust tooth classification, with the potential to improve dental diagnosis and treatment planning.
Keywords: Dentistry, classification, deep learning, artificial intelligence, radiography

Anahtar Kelimeler

Dentistry classification deep learning artificial intelligence

Kaynakça

  • 1. Im J, Kim JY, Yu HS, et al. Accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning. Sci Rep. 2022;12(1):9429. doi:10.1038/s41598-022-13595-2
  • 2. Liu M, Wang S, Chen H, Liu Y. A pilot study of a deep learning approach to detect marginal bone loss around implants. BMC Oral Health. 2022;22(1):11. doi:10.1186/s12903-021-02035-8
  • 3. Choi HR, Siadari TS, Kim JE, et al. Automatic detection of teeth and dental treatment patterns on dental panoramic radiographs using deep neural networks. Forensic Sci Res. 2022;7(3):456-466. doi:10.1080/20961790.2022.2034714
  • 4. Kaya M, Bilge HS. Classification of pancreas tumor dataset using adaptive weighted k nearest neighbor algorithm. 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings. 2014:253-257. doi:10.1109/inista.2014.6873626
  • 5. Kaya M, Bilge HS. Classification of Parkinson speech data by metric learning. 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). 2017:1-5. doi:10.1109/idap.2017.8090285
  • 6. Chollet F. Xception: Deep Learning with Depthwise Separable Convolutions. arXiv.org. 2016. https://arxiv.org/abs/1610.02357
  • 7. Szegedy C, Liu W, Jia Y, et al. Going Deeper with Convolutions. arXiv.org. 2014. https://arxiv.org/abs/1409.4842
  • 8. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. arXiv.org. 2015. https://arxiv.org/abs/1512.03385
  • 9. Zhang X, Zhou X, Lin M, Sun J. ShuffleNet: an extremely efficient convolutional neural network for mobile devices. arXiv.org. 2017. https://arxiv.org/abs/1707.01083
  • 10. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. MobileNetV2: Inverted residuals and linear bottlenecks. arXiv.org. 2018. https://arxiv.org/abs/1801.04381
  • 11. Xie S, Girshick R, Dollár P, Tu Z, He K. Aggregated residual transformations for deep neural networks. arXiv.org. 2016. https://doi.org/10.48550/arXiv.1611.05431
  • 12. Mohammad-Rahimi H, Motamedian SR, Rohban MH, et al. Deep learning for caries detection: A systematic review. J Dent. 2022;122:104115. doi:10.1016/j.jdent.2022.104115
  • 13. Çelik B, Çelik ME. Automated detection of dental restorations using deep learning on panoramic radiographs. Dentomaxillofac Radiol. 2022;51(8):20220244. doi:10.1259/dmfr.20220244
  • 14. Chang HJ, Lee SJ, Yong TH, et al. Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis. Sci Rep. 2020;10(1):7531. doi:10.1038/s41598-020-64509-z
  • 15. Çelik B, Çelik ME. Root Dilaceration Using Deep Learning: A Diagnostic Approach. Appl. Sci. 2023;13(14):8260.
  • 16. Mohammad-Rahimi H, Rokhshad R, Bencharit S, Krois J, Schwendicke F. Deep learning: A primer for dentists and dental researchers. J Dent. 2023;130:104430.
  • 17. Celik ME. Deep learning based detection tool for impacted mandibular third molar teeth. Diagnostics. 2022;12(4):942. doi:10.3390/diagnostics12040942
  • 18. Umer F, Habib S, Adnan N. Application of deep learning in teeth identification tasks on panoramic radiographs. Dentomaxillofac Radiol. 2022;51(5): 20210504. doi:10.1259/dmfr.20210504
  • 19. Çelik B, Savaştaer EF, Kaya HI, Çelik ME. The role of deep learning for periapical lesion detection on panoramic radiographs. Dentomaxillofac Radiol. 2023;52(8):20230118. doi:10.1259/dmfr.20230118
  • 20. Mureșanu S, Almășan O, Hedeșiu M, Dioșan L, Dinu C, Jacobs R. Artificial intelligence models for clinical usage in dentistry with a focus on dentomaxillofacial CBCT: a systematic review. Oral Radiol. 2023;39(1):18-40. doi:10.1007/s11282-022-00660-9
  • 21. Li Z, Wang S, Fan R, Cao G, Zhang Y, Guo T. Teeth category classification via seven‐layer deep convolutional neural network with max pooling and global average pooling. Int J Imaging Syst Technol. 2019;29(4):577-583. doi:10.1002/ima.22337
  • 22. Sukegawa S, Matsuyama T, Tanaka F, et al. Evaluation of multi-task learning in deep learning-based positioning classification of mandibular third molars. Sci Rep. 2022;12(1):684. doi:10.1038/s41598-021-04603-y
  • 23. Estai M, Tennant M, Gebauer D, et al. Deep learning for automated detection and numbering of permanent teeth on panoramic images. Dentomaxillofac Radiol. 2022;51(2):20210296. doi:10.1259/dmfr.20210296
  • 24. Krois J, Schneider L, Schwendicke F. Impact of Image Context on Deep Learning for Classification of Teeth on Radiographs. J Clin Med. 2021;10(8):1635. doi:10.3390/jcm10081635
  • 25. Panetta K, Rajendran R, Ramesh A, Rao S, Agaian S. Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems. IEEE J Biomed Health Inform. 2022;26(4):1650-1659. doi:10.1109/JBHI.2021.3117575
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ağız, Diş ve Çene Radyolojisi
Bölüm Araştırma Makalesi
Yazarlar

Berrin Çelik

Fikret Ulus

Ertuğrul Furkan Savaştaer

Mehmet Zahid Genç

Mahmut Emin Çelik

Yayımlanma Tarihi 20 Nisan 2025
Gönderilme Tarihi 24 Ekim 2023
Kabul Tarihi 18 Ocak 2024
Yayımlandığı Sayı Yıl 2025 Cilt: 35 Sayı: 2

Kaynak Göster

AMA Çelik B, Ulus F, Savaştaer EF, Genç MZ, Çelik ME. Image Processing for Tooth Type Classification using Deep Learning. Curr Res Dent Sci. Nisan 2025;35(2):137-141. doi:10.17567/currresdentsci.1677708
 Kapak Resmi İndir

Current Research in Dental Sciences is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

29936