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

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

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

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

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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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
Türkiye

Yasin Özkan
0000-0002-2029-0856
Türkiye

Yayımlanma Tarihi

3 Mart 2026

Gönderilme Tarihi

8 Ağustos 2025

Kabul Tarihi

28 Kasım 2025

Yayımlandığı Sayı

Yıl 2026 Cilt: 29 Sayı: 1

DOI

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

IZ

https://izlik.org/JA74CS79GD

Kaynak Göster

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