Araştırma Makalesi
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Classification of Cervical Vertebral Maturation Stages and Bone Age Assessment Using Transfer Learning–Based Deep-Learning Approaches

Yıl 2025, Cilt: 4 Sayı: 2, 393 - 405, 26.06.2025

Mazhar Kayaoğlu , Abdülkadir Şengür , Sabahattin Bor , Seda Kotan

https://doi.org/10.62520/fujece.1657886

Öz

In this study, an automatic classification of cervical vertebra maturation (CVM) stages was performed using raw lateral cephalometric radiographs to assess growth and development. A total of 4285 radiographs from the Department of Orthodontics at Van Yüzüncü Yıl University Faculty of Dentistry were utilized. Following detailed evaluations by specialist physicians, 3750 images meeting diagnostic accuracy and clinical suitability criteria were included. The selected images were categorized into six classes (CVMS 1–6), forming a balanced dataset for classification with the NFNet, ConvNeXt V2, EfficientNet V2, and DeiT3 models. The NFNet model achieved the highest overall performance, with 96% training accuracy and 85.7% test accuracy. ConvNeXt V2, attaining 95% training accuracy and 86.9% test accuracy, emerged as the most balanced in terms of generalization. Although EfficientNet V2 reached 94% training accuracy, its 80.7% test accuracy indicated limited generalization. With 93% training accuracy and 77.6% test accuracy, DeiT3 demonstrated the lowest capacity. Both NFNet and ConvNeXt V2 stood out as strong classification candidates based on their high accuracy and balanced performance. While NFNet showed a 10.3% gap between training and test accuracy, indicating somewhat reduced generalization, ConvNeXt V2’s narrower 8.1% gap suggested greater stability. In conclusion, NFNet and ConvNeXt V2 are promising models for CVM classification. Future studies should employ larger datasets and conduct hyperparameter optimization to enhance these models’ performance and strengthen their clinical applicability.

Anahtar Kelimeler

Image classification Transfer learning Cervical vertebral maturation

Etik Beyan

In this study, all relevant legal and ethical considerations were observed during the collection and evaluation of data from human participants. All procedures related to the research were approved by the Van Yüzüncü Yıl University Non-Invasive Clinical Research Ethics Committee on September 18, 2023 (Decision No. 2023/09-12). The research was carried out in line with the principles set forth in the Declaration of Helsinki, and appropriate data protection measures were taken to safeguard participant confidentiality. Furthermore, no conflict of interest exists with any individual, institution, or organization in the planning, execution, data analysis, or reporting stages of this study. All authors confirm their adherence to research ethics throughout every phase of the work.

Kaynakça

  • S. F. Atici et al., “A collaborative fusion of vision transformers and convolutional neural networks in classifying cervical vertebrae maturation stages,” in Proc. 2023 30th IEEE Int. Conf. on Electronics, Circuits and Systems (ICECS), 2023, pp. 1–4.
  • M. T. Radwan, Ç. Sin, N. Akkaya, and L. Vahdettin, “Artificial intelligence-based algorithm for cervical vertebrae maturation stage assessment,” Orthod. Craniofac. Res., vol. 26, no. 3, pp. 349–355, 2023.
  • H. Li et al., “Convolutional neural network-based automatic cervical vertebral maturation classification method,” Dentomaxillofac. Radiol., vol. 51, no. 6, p. 20220070, 2022.
  • H. Seo, J.-H. Kim, S.-H. Lee, and Y. H. Kim, “Comparison of deep learning models for cervical vertebral maturation stage classification on lateral cephalometric radiographs,” J. Clin. Med., vol. 10, no. 16, p. 3591, 2021.
  • M. S. İzgi and H. Kök, “Kemik yaşı ve maturasyon tespiti,” Selçuk Dental J., vol. 7, no. 1, pp. 124–133, 2020.
  • J. A. McNamara Jr. and L. Franchi, “The cervical vertebral maturation method: A user's guide,” Angle Orthod., vol. 88, no. 2, pp. 133–143, 2018.
  • S. F. Atici et al., “A novel continuous classification system for the cervical vertebrae maturation (CVM) stages using convolutional neural networks,” 2023.
  • M. Khazaei et al., “Automatic determination of pubertal growth spurts based on the cervical vertebral maturation staging using deep convolutional neural networks,” J. World Fed. Orthod., vol. 12, no. 2, pp. 56–63, 2023.
  • S. F. Atici et al., “Classification of the cervical vertebrae maturation (CVM) stages using the tripod network,” in Proc. ICASSP 2023–IEEE Int. Conf. Acoust., Speech, Signal Process., 2023, pp. 1–5.
  • G. A. Kresnadhi et al., “Comparative analysis of ResNet101, InceptionV3, and InceptionResNetV2 architectures for cervical vertebrae maturation stage classification,” in Proc. 2023 Int. Conf. Electr. Eng. Informatics (ICEEI), 2023.
  • M. H. Mohammed et al., “Convolutional neural network-based deep learning methods for skeletal growth prediction in dental patients,” J. Imaging, vol. 10, no. 11, p. 278, 2024.
  • G. Akay et al., “Deep convolutional neural network—the evaluation of cervical vertebrae maturation,” Oral Radiol., vol. 39, no. 4, pp. 629–638, 2023.
  • M. Makaremi, C. Lacaule, and A. Mohammad-Djafari, “Deep learning and artificial intelligence for the determination of the cervical vertebra maturation degree from lateral radiography,” Entropy, vol. 21, no. 12, p. 1222, 2019.
  • P. Motie, A. Kamali, A. Rahimi, and H. Rahimi, “Improving cervical maturation degree classification accuracy using a multi-stage deep learning approach,” 2024.
  • S. F. Atici et al., “AggregateNet: A deep learning model for automated classification of cervical vertebrae maturation stages,” Orthod. Craniofac. Res., vol. 26, pp. 111–117, 2023.
  • H. Li et al., “The psc-CVM assessment system: A three-stage type system for CVM assessment based on deep learning,” BMC Oral Health, vol. 23, no. 1, p. 557, 2023.
  • S. Woo et al., “ConvNeXt V2: Co-designing and scaling convnets with masked autoencoders,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2023, pp. 16133–16142.
  • H. Touvron, M. Cord, and H. Jégou, “DeiT III: Revenge of the ViT,” in Proc. Eur. Conf. Comput. Vis., Cham: Springer Nature Switzerland, 2022, pp. 516–533.
  • M. Tan and Q. Le, “EfficientNetV2: Smaller models and faster training,” in Proc. Int. Conf. Mach. Learn., PMLR, 2021, pp. 10096–10106.
  • A. Brock, S. De, S. L. Smith, and K. Simonyan, “High-performance large-scale image recognition without normalization,” in Proc. Int. Conf. Mach. Learn., PMLR, 2021, pp. 1059–1071.

Transfer Öğrenme Tabanlı Derin Öğrenme Yaklaşımlarıyla Servikal Vertebra Matürasyon Safhalarının Sınıflandırılması ve Kemik Yaşı Değerlendirilmesi

Yıl 2025, Cilt: 4 Sayı: 2, 393 - 405, 26.06.2025

Mazhar Kayaoğlu , Abdülkadir Şengür , Sabahattin Bor , Seda Kotan

https://doi.org/10.62520/fujece.1657886

Öz

Bu çalışmada, büyüme ve gelişimi değerlendirmek amacıyla lateral sefalometrik radyografiler kullanılarak servikal vertebra maturasyon (CVM) evrelerinin otomatik sınıflandırılması gerçekleştirilmiştir. Van Yüzüncü Yıl Üniversitesi Diş Hekimliği Fakültesi Ortodonti Anabilim Dalı tarafından sağlanan toplam 4285 radyografi kullanılmıştır. Uzman hekimler tarafından yapılan detaylı değerlendirmeler sonucunda, tanısal doğruluk ve klinik uygunluk kriterlerini karşılayan 3750 görüntü çalışmaya dâhil edilmiştir. Seçilen görüntüler, altı sınıfa (CVMS 1–6) ayrılarak dengeli bir veri seti oluşturulmuş ve NFNet, ConvNeXt V2, EfficientNet V2 ve DeiT3 modelleri kullanılarak sınıflandırma işlemleri gerçekleştirilmiştir. NFNet modeli, %96 eğitim doğruluğu ve %85,7 test doğruluğu ile en yüksek genel performansı sergilemiştir. %95 eğitim doğruluğu ve %86,9 test doğruluğu elde eden ConvNeXt V2, genelleme açısından en dengeli model olarak öne çıkmıştır. EfficientNet V2, %94 eğitim doğruluğuna ulaşmasına rağmen %80,7 test doğruluğu ile sınırlı bir genelleme kapasitesi göstermiştir. DeiT3 modeli ise %93 eğitim doğruluğu ve %77,6 test doğruluğu ile en düşük genelleme kapasitesine sahip olmuştur. NFNet ve ConvNeXt V2, yüksek doğruluk oranları ve dengeli performansları sayesinde güçlü sınıflandırma adayları olarak öne çıkmıştır. NFNet’in eğitim ve test doğruluğu arasındaki %10,3’lük fark genelleme kapasitesinde bir miktar azalmaya işaret ederken, ConvNeXt V2’nin daha dar olan %8,1’lik farkı daha istikrarlı bir performans göstermiştir. Sonuç olarak, NFNet ve ConvNeXt V2, CVM sınıflandırması için umut vadeden modeller olarak belirlenmiştir. Gelecekteki çalışmalarda, bu modellerin performansını artırmak ve klinik uygulanabilirliklerini güçlendirmek için daha büyük veri setleri kullanılması ve hiperparametre optimizasyonunun gerçekleştirilmesi önerilmektedir.

Anahtar Kelimeler

Görüntü sınıflandırma Transfer öğrenimi Servikal vertebra matürasyonu

Etik Beyan

Bu çalışmada, insan katılımcılardan veri toplanması ve değerlendirilmesi sırasında tüm ilgili yasal ve etik hususlar gözetilmiştir. Araştırmayla ilgili tüm prosedürler Van Yüzüncü Yıl Üniversitesi Girişimsel Olmayan Klinik Araştırmalar Etik Kurulu tarafından 18 Eylül 2023 tarihinde onaylanmıştır (Karar No. 2023/09-12). Araştırma Helsinki Bildirgesi'nde belirtilen ilkelere uygun olarak yürütülmüş olup, katılımcı gizliliğini korumak için uygun veri koruma önlemleri alınmıştır. Ayrıca, bu çalışmanın planlanması, yürütülmesi, veri analizi veya raporlama aşamalarında hiçbir kişi, kurum veya kuruluşla çıkar çatışması bulunmamaktadır. Tüm yazarlar, çalışmanın her aşamasında araştırma etiğine bağlı kaldıklarını teyit etmektedir.

Kaynakça

  • S. F. Atici et al., “A collaborative fusion of vision transformers and convolutional neural networks in classifying cervical vertebrae maturation stages,” in Proc. 2023 30th IEEE Int. Conf. on Electronics, Circuits and Systems (ICECS), 2023, pp. 1–4.
  • M. T. Radwan, Ç. Sin, N. Akkaya, and L. Vahdettin, “Artificial intelligence-based algorithm for cervical vertebrae maturation stage assessment,” Orthod. Craniofac. Res., vol. 26, no. 3, pp. 349–355, 2023.
  • H. Li et al., “Convolutional neural network-based automatic cervical vertebral maturation classification method,” Dentomaxillofac. Radiol., vol. 51, no. 6, p. 20220070, 2022.
  • H. Seo, J.-H. Kim, S.-H. Lee, and Y. H. Kim, “Comparison of deep learning models for cervical vertebral maturation stage classification on lateral cephalometric radiographs,” J. Clin. Med., vol. 10, no. 16, p. 3591, 2021.
  • M. S. İzgi and H. Kök, “Kemik yaşı ve maturasyon tespiti,” Selçuk Dental J., vol. 7, no. 1, pp. 124–133, 2020.
  • J. A. McNamara Jr. and L. Franchi, “The cervical vertebral maturation method: A user's guide,” Angle Orthod., vol. 88, no. 2, pp. 133–143, 2018.
  • S. F. Atici et al., “A novel continuous classification system for the cervical vertebrae maturation (CVM) stages using convolutional neural networks,” 2023.
  • M. Khazaei et al., “Automatic determination of pubertal growth spurts based on the cervical vertebral maturation staging using deep convolutional neural networks,” J. World Fed. Orthod., vol. 12, no. 2, pp. 56–63, 2023.
  • S. F. Atici et al., “Classification of the cervical vertebrae maturation (CVM) stages using the tripod network,” in Proc. ICASSP 2023–IEEE Int. Conf. Acoust., Speech, Signal Process., 2023, pp. 1–5.
  • G. A. Kresnadhi et al., “Comparative analysis of ResNet101, InceptionV3, and InceptionResNetV2 architectures for cervical vertebrae maturation stage classification,” in Proc. 2023 Int. Conf. Electr. Eng. Informatics (ICEEI), 2023.
  • M. H. Mohammed et al., “Convolutional neural network-based deep learning methods for skeletal growth prediction in dental patients,” J. Imaging, vol. 10, no. 11, p. 278, 2024.
  • G. Akay et al., “Deep convolutional neural network—the evaluation of cervical vertebrae maturation,” Oral Radiol., vol. 39, no. 4, pp. 629–638, 2023.
  • M. Makaremi, C. Lacaule, and A. Mohammad-Djafari, “Deep learning and artificial intelligence for the determination of the cervical vertebra maturation degree from lateral radiography,” Entropy, vol. 21, no. 12, p. 1222, 2019.
  • P. Motie, A. Kamali, A. Rahimi, and H. Rahimi, “Improving cervical maturation degree classification accuracy using a multi-stage deep learning approach,” 2024.
  • S. F. Atici et al., “AggregateNet: A deep learning model for automated classification of cervical vertebrae maturation stages,” Orthod. Craniofac. Res., vol. 26, pp. 111–117, 2023.
  • H. Li et al., “The psc-CVM assessment system: A three-stage type system for CVM assessment based on deep learning,” BMC Oral Health, vol. 23, no. 1, p. 557, 2023.
  • S. Woo et al., “ConvNeXt V2: Co-designing and scaling convnets with masked autoencoders,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2023, pp. 16133–16142.
  • H. Touvron, M. Cord, and H. Jégou, “DeiT III: Revenge of the ViT,” in Proc. Eur. Conf. Comput. Vis., Cham: Springer Nature Switzerland, 2022, pp. 516–533.
  • M. Tan and Q. Le, “EfficientNetV2: Smaller models and faster training,” in Proc. Int. Conf. Mach. Learn., PMLR, 2021, pp. 10096–10106.
  • A. Brock, S. De, S. L. Smith, and K. Simonyan, “High-performance large-scale image recognition without normalization,” in Proc. Int. Conf. Mach. Learn., PMLR, 2021, pp. 1059–1071.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı, Otomatik Yazılım Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

Mazhar Kayaoğlu 0000-0002-5807-9781

Abdülkadir Şengür 0000-0003-1614-2639

Sabahattin Bor 0000-0001-5463-0057

Seda Kotan 0000-0003-3405-4851

Yayımlanma Tarihi 26 Haziran 2025
Gönderilme Tarihi 14 Mart 2025
Kabul Tarihi 30 Mayıs 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 4 Sayı: 2

Kaynak Göster

APA Kayaoğlu, M., Şengür, A., Bor, S., Kotan, S. (2025). Classification of Cervical Vertebral Maturation Stages and Bone Age Assessment Using Transfer Learning–Based Deep-Learning Approaches. Firat University Journal of Experimental and Computational Engineering, 4(2), 393-405. https://doi.org/10.62520/fujece.1657886
AMA Kayaoğlu M, Şengür A, Bor S, Kotan S. Classification of Cervical Vertebral Maturation Stages and Bone Age Assessment Using Transfer Learning–Based Deep-Learning Approaches. FUJECE. Haziran 2025;4(2):393-405. doi:10.62520/fujece.1657886
Chicago Kayaoğlu, Mazhar, Abdülkadir Şengür, Sabahattin Bor, ve Seda Kotan. “Classification of Cervical Vertebral Maturation Stages and Bone Age Assessment Using Transfer Learning–Based Deep-Learning Approaches”. Firat University Journal of Experimental and Computational Engineering 4, sy. 2 (Haziran 2025): 393-405. https://doi.org/10.62520/fujece.1657886.
EndNote Kayaoğlu M, Şengür A, Bor S, Kotan S (01 Haziran 2025) Classification of Cervical Vertebral Maturation Stages and Bone Age Assessment Using Transfer Learning–Based Deep-Learning Approaches. Firat University Journal of Experimental and Computational Engineering 4 2 393–405.
IEEE M. Kayaoğlu, A. Şengür, S. Bor, ve S. Kotan, “Classification of Cervical Vertebral Maturation Stages and Bone Age Assessment Using Transfer Learning–Based Deep-Learning Approaches”, FUJECE, c. 4, sy. 2, ss. 393–405, 2025, doi: 10.62520/fujece.1657886.
ISNAD Kayaoğlu, Mazhar vd. “Classification of Cervical Vertebral Maturation Stages and Bone Age Assessment Using Transfer Learning–Based Deep-Learning Approaches”. Firat University Journal of Experimental and Computational Engineering 4/2 (Haziran 2025), 393-405. https://doi.org/10.62520/fujece.1657886.
JAMA Kayaoğlu M, Şengür A, Bor S, Kotan S. Classification of Cervical Vertebral Maturation Stages and Bone Age Assessment Using Transfer Learning–Based Deep-Learning Approaches. FUJECE. 2025;4:393–405.
MLA Kayaoğlu, Mazhar vd. “Classification of Cervical Vertebral Maturation Stages and Bone Age Assessment Using Transfer Learning–Based Deep-Learning Approaches”. Firat University Journal of Experimental and Computational Engineering, c. 4, sy. 2, 2025, ss. 393-05, doi:10.62520/fujece.1657886.
Vancouver Kayaoğlu M, Şengür A, Bor S, Kotan S. Classification of Cervical Vertebral Maturation Stages and Bone Age Assessment Using Transfer Learning–Based Deep-Learning Approaches. FUJECE. 2025;4(2):393-405.
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