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

Fatma Akalın; Yasin Özkan

doi:10.17780/ksujes.1760369

TR EN

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

Abstract

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.

Keywords

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

Abstract

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.

Keywords

Thanks

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.

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Details

Primary Language

English

Subjects

Image Processing, Pattern Recognition, Deep Learning, Neural Networks, Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Fatma Akalın ^*
0000-0001-6670-915X
Türkiye

Yasin Özkan
0000-0002-2029-0856
Türkiye

Publication Date

March 3, 2026

Submission Date

August 8, 2025

Acceptance Date

November 28, 2025

Published in Issue

Year 2026 Volume: 29 Number: 1

DOI

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

IZ

https://izlik.org/JA74CS79GD

Cite

RIS / Bibtex

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

AMA

1.Akalın F, Özkan Y. AUTOMATIC CLASSIFICATION SYSTEM FOR PERIAPICAL LESIONS WITH THE TOOTHNET CNN ARCHITECTURE INTEGRATING THE PROPOSED HYBRID ACTIVATION FUNCTION ON CBCT SCANS. KSU J. Eng. Sci. 2026;29(1):75-93. doi:10.17780/ksujes.1760369

Chicago

Akalın, Fatma, and Yasin Özkan. 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.

EndNote

Akalın F, Özkan Y (March 1, 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.

IEEE

[1]F. Akalın and Y. Özkan, “AUTOMATIC CLASSIFICATION SYSTEM FOR PERIAPICAL LESIONS WITH THE TOOTHNET CNN ARCHITECTURE INTEGRATING THE PROPOSED HYBRID ACTIVATION FUNCTION ON CBCT SCANS”, KSU J. Eng. Sci., vol. 29, no. 1, pp. 75–93, Mar. 2026, doi: 10.17780/ksujes.1760369.

ISNAD

Akalın, Fatma - Özkan, Yasin. “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 (March 1, 2026): 75-93. https://doi.org/10.17780/ksujes.1760369.

JAMA

1.Akalın F, Özkan Y. AUTOMATIC CLASSIFICATION SYSTEM FOR PERIAPICAL LESIONS WITH THE TOOTHNET CNN ARCHITECTURE INTEGRATING THE PROPOSED HYBRID ACTIVATION FUNCTION ON CBCT SCANS. KSU J. Eng. Sci. 2026;29:75–93.

MLA

Akalın, Fatma, and Yasin Özkan. “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, vol. 29, no. 1, Mar. 2026, pp. 75-93, doi:10.17780/ksujes.1760369.

Vancouver

1.Fatma Akalın, Yasin Özkan. AUTOMATIC CLASSIFICATION SYSTEM FOR PERIAPICAL LESIONS WITH THE TOOTHNET CNN ARCHITECTURE INTEGRATING THE PROPOSED HYBRID ACTIVATION FUNCTION ON CBCT SCANS. KSU J. Eng. Sci. 2026 Mar. 1;29(1):75-93. doi:10.17780/ksujes.1760369