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CBCT TARAMALARINDA ÖNERILEN HIBRIT AKTIVASYON FONKSIYONUNU ENTEGRE EDEN TOOTHNET CNN MIMARISI ILE PERIAPIKAL LEZYONLAR IÇIN OTOMATIK SINIFLANDIRMA SISTEMI

Yıl 2026, Cilt: 29 Sayı: 1, 75 - 93, 03.03.2026

Fatma Akalın , Yasin Özkan

https://doi.org/10.17780/ksujes.1760369
https://izlik.org/JA74CS79GD

Öz

Görüntüleme teknikleri, dişlerin 3 boyutlu yapısını anlamak ve hastalıkları tespit etmek için diş hekimliğinde yaygın olarak kullanılmaktadır, ancak bunların yorumlanması zaman alıcıdır ve hataya açıktır. Bu sorunu çözmek için karar destek sistemleri giderek daha fazla kullanılmaktadır. Bu çalışma, Konik Işınlı Bilgisayarlı Tomografi (CBCT) taramalarını içeren UFPE veri setini kullanan CNN tabanlı bir sınıflandırma modeli önermektedir. İlk senaryoda, hem gerçek hem de geliştirilmiş görüntüler bir CNN'ye girildi ve geliştirilmiş görüntüler için %68,92 doğruluk oranı elde edildi. Bu iyileştirme nedeniyle, diğer tüm senaryolarda geliştirilmiş görüntüler kullanıldı. İkinci senaryoda, özel bir aktivasyon fonksiyonu içeren, ToothNet adlı yeni tasarlanmış bir CNN mimarisi test edildi. Bu mimari %69,92 doğruluk, %61,45 geri çağırma, %62,67 kesinlik ve %68,68 F1 puanı elde ederek %1,45 doğruluk artışı gösterdi. Genelleştirilebilirliği değerlendirmek için, aynı veri seti kullanılarak üç sınıflandırma senaryosu daha incelendi. ToothNet, “sağlıklı vs. büyük lezyon” sınıflandırmasında %80,14 doğruluk ve “sağlıklı vs. küçük lezyon” sınıflandırmasında %68,73 doğruluk elde etti. Bu sonuçlar, önerilen mimarinin sadece doğruluğu artırmakla kalmayıp, farklı lezyon boyutları arasında da genelleştirilebilir olduğunu göstermektedir.

Anahtar Kelimeler

CBCT görüntüleme yaklaşımı Orijinal görüntü işleme yöntemi ToothNet CNN mimarisi Önerilen Hibrit Aktivasyon Fonksiyonu normal ve lezyon görüntülerinin sınıflandırılması

Kaynakça

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AUTOMATIC CLASSIFICATION SYSTEM FOR PERIAPICAL LESIONS WITH THE TOOTHNET CNN ARCHITECTURE INTEGRATING THE PROPOSED HYBRID ACTIVATION FUNCTION ON CBCT SCANS

Yıl 2026, Cilt: 29 Sayı: 1, 75 - 93, 03.03.2026

Fatma Akalın , Yasin Özkan

https://doi.org/10.17780/ksujes.1760369
https://izlik.org/JA74CS79GD

Öz

Imaging techniques are widely used in dentistry to understand the 3D structure of teeth and detect diseases, but their interpretation is time-consuming and prone to error. To address this, decision support systems are increasingly utilized. This study proposes a CNN-based classification model using the UFPE dataset, which includes Cone Beam Computed Tomography (CBCT) scans. In the first scenario, both real and enhanced images were input into a CNN, yielding 68.92% accuracy for enhanced images. Due to a result, enhanced images were used in all other scenarios. In the second scenario, a newly designed CNN architecture called ToothNet, incorporating a custom activation function, was tested. It achieved 69.92% accuracy, 61.45% recall, 62.67% precision, and 68.68% F1-score, showing a 1.45% increase in accuracy. To evaluate generalizability, three more classification scenarios were examined using the same dataset. ToothNet achieved 80.14% accuracy in the “healthy vs. large lesion” and 68.73% in the “healthy vs. small lesion” classification. These results indicate that the proposed architecture not only improves accuracy but is also generalizable across different lesion sizes.

Anahtar Kelimeler

CBCT imaging approach Original image processing method ToothNet CNN architecture Proposed Hybrid Activation Function classification of normal and lesion images.

Teşekkür

We gratefully acknowledge the assistance of ChatGPT -3.5 and ChatGBT-4o-mini language models for its contributions to this study. The tool provided valuable support in writing some code parts, providing different perspectives, analyzing mathematical equations, identifying and addressing coding errors with ease, and ensuring accurate and clear translations. The authors have reviewed and edited the output and take full responsibility for the content of this publication.

Kaynakça

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  • Ahmad, S. A., Taib, M. N., Khalid, N. E. A., & Taib, H. (2012). An analysis of image enhancement techniques for dental X-ray image interpretation. International Journal of Machine Learning and Computing, 2(3), 292.
  • Akalın, F., & Orhan, M. F., Önerilen Görüntü İşleme Yaklaşımı ile Optimize Edilen Dijital Blast Hücre Görüntülerinin Mobılenetv2 Transfer Öğrenme Mimirisi ile Sınıflandırılması, Munzur Internatıonal Scıentıfıc Research and Innovatıon Congress. August 06-07, p.849,858, 2024 / Tunceli, Türkiye.
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  • Akalin, F., & Yildiz, T. (2025). Detection and classification of enhanced periapical lesion images with YOLO algorithms. Connection Science, 37(1). https://doi.org/10.1080/09540091.2025.2522706
  • Aksoylu, M. (2021). Görüntü işleme ve bilgisayarla görü. ABC Yayınları. ISBN: 978-1234567890.
  • Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., ... & Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of big Data, 8, 1-74. https://doi.org/10.1186/s40537-021-00444-8
  • Apicella, A., Donnarumma, F., Isgrò, F., & Prevete, R. (2021). A survey on modern trainable activation functions. Neural Networks, 138, 14-32. https://doi.org/10.1016/j.neunet.2021.01.026
  • Benington, P. C., Khambay, B. S., & Ayoub, A. F. (2010). An overview of three-dimensional imaging in dentistry. Dental update, 37(8), 494-508. https://doi.org/10.12968/denu.2010.37.8.494
  • Calazans, M. A. A., Ferreira, F. A. B., Alcoforado, M. D. L. M. G., Santos, A. D., Pontual, A. D. A., & Madeiro, F. (2022). Automatic classification system for periapical lesions in cone-beam computed tomography. Sensors, 22(17), 6481. https://doi.org/10.3390/s22176481
  • Cejudo, J. E., Chaurasia, A., Feldberg, B., Krois, J., & Schwendicke, F. (2021). Classification of dental radiographs using deep learning. Journal of Clinical Medicine, 10(7), 1496. https://doi.org/10.3390/jcm10071496
  • Chauhan, R. B., Shah, T. V., Shah, D. H., Gohil, T. J., Oza, A. D., Jajal, B., & Saxena, K. K. (2023). An overview of image processing for dental diagnosis. Innovation and Emerging Technologies, 10, 2330001. https://doi.org/10.1142/S2737599423300015
  • Chuo, Y., Lin, W. M., Chen, T. Y., Chan, M. L., Chang, Y. S., Lin, Y. R., ... & Chen, S. L. A high-accuracy detection system: Based on transfer learning for apical lesions on periapical radiograph., 2022, 9, 777. DOI: https://doi. org/10.3390/bioengineering9120777.
  • Cotti, E., & Schirru, E. (2022). Present status and future directions: Imaging techniques for the detection of periapical lesions. International Endodontic Journal, 55, 1085-1099. https://doi.org/10.1111/iej.13828
  • Endres, M.G.; Hillen, F.; Salloumis, M.; Sedaghat, A.R.; Niehues, S.M.; Quatela, O.; Hanken, H.; Smeets, R.; Beck-Broichsitter, B.; Rendenbach, C.; et al. Development of a deep learning algorithm for periapical disease detection in dental radiographs. Diagnostics 2020, 10, 430. https://doi.org/10.3390/diagnostics10060430
  • Esmaeilyfard, R., Bonyadifard, H., & Paknahad, M. (2024). Dental Caries Detection and Classification in CBCT Images Using Deep Learning. international dental journal, 74(2), 328-334. https://doi.org/10.1016/j.identj.2023.10.003
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Toplam 68 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Görüntü İşleme, Örüntü Tanıma, Derin Öğrenme, Nöral Ağlar, Yapay Zeka (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Fatma Akalın 0000-0001-6670-915X

Yasin Özkan 0000-0002-2029-0856

Gönderilme Tarihi 8 Ağustos 2025
Kabul Tarihi 28 Kasım 2025
Yayımlanma Tarihi 3 Mart 2026
DOI https://doi.org/10.17780/ksujes.1760369
IZ https://izlik.org/JA74CS79GD
Yayımlandığı Sayı Yıl 2026 Cilt: 29 Sayı: 1

Kaynak Göster

APA Akalın, F., & Özkan, Y. (2026). AUTOMATIC CLASSIFICATION SYSTEM FOR PERIAPICAL LESIONS WITH THE TOOTHNET CNN ARCHITECTURE INTEGRATING THE PROPOSED HYBRID ACTIVATION FUNCTION ON CBCT SCANS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 29(1), 75-93. https://doi.org/10.17780/ksujes.1760369