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

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

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.

References

A. Radhiyah, T. Harsono & R. Sigit, "Comparison study of Gaussian and histogram equalization filter on dental radiograph segmentation for labelling dental radiograph," 2016 International Conference on Knowledge Creation and Intelligent Computing (KCIC), Manado, Indonesia, 2016, pp. 253-258, doi: 10.1109/KCIC.2016.7883655.
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.
Akalın, F., & Yumuşak, N. (2024). Derin öğrenme tabanlı topluluk sınıflandırıcı yaklaşımı ile gastrointestinal anomalilerin tespiti. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 30(3), 366-373. doi: 10.5505/pajes.2023.90602
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
Esteva, A., Kuprel, B., Novoa, R. A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118. https://doi.org/10.1038/nature21056
Flores, A., Rysavy, S., Enciso, R., & Okada, K. (2009, June). Non-invasive differential diagnosis of dental periapical lesions in cone-beam CT. In 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (pp. 566-569). IEEE. doi: 10.1109/ISBI.2009.5193110.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. https://doi.org/10.4258/hir.2016.22.4.351
Gonzalez, R. C., & Woods, R. E. (2014). Digital image processing (4th ed.). Pearson.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770-778).
Hashimoto, D. A., Ward, T. M., & Meireles, O. R. (2020). The role of artificial intelligence in surgery. Advances in Surgery, 54, 89-101. DOI: 10.1016/j.yasu.2020.05.010
Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the 32nd International Conference on Machine Learning (ICML) (pp. 448-456).
J. Li, X. Feng and H. Fan, "Saliency Consistency-Based Image Re-Colorization for Color Blindness," in IEEE Access, vol. 8, pp. 88558-88574, 2020, doi: 10.1109/ACCESS.2020.2993300.
Juliastuti, E., & Epsilawati, L. (2012, September). Image contrast enhancement for film-based dental panoramic radiography. In 2012 International Conference on System Engineering and Technology (ICSET) (pp. 1-5). IEEE. doi: 10.1109/ICSEngT.2012.6339321.
Khan, R., Akbar, S., Khan, A., Marwan, M., Qaisar, Z. H., Mehmood, A., ... & Zheng, Z. (2023). Dental image enhancement network for early diagnosis of oral dental disease. Scientific Reports, 13(1), 5312. https://doi.org/10.1038/s41598-023-30548-5
Krijevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (NeurIPS) (pp. 1097-1105).
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436-444. https://doi.org/10.1038/nature14539
Lee, S., Woo, S., Yu, J., Seo, J., Lee, J., & Lee, C. (2020). Automated CNN-based tooth segmentation in cone-beam CT for dental implant planning. IEEE Access, 8, 50507-50518. doi: 10.1109/ACCESS.2020.2975826
Li, C. W., Lin, S. Y., Chou, H. S., Chen, T. Y., Chen, Y. A., Liu, S. Y., ... & Lo, W. S. (2021). Detection of dental apical lesions using CNNs on periapical radiograph. Sensors, 21(21), 7049. https://doi.org/10.3390/s21217049
Lin, P. L., Huang, P. W., Cho, Y. S., & Kuo, C. H. (2013). An automatic and effective tooth isolation method for dental radiographs. Opto-Electronics Review, 21, 126-136. https://doi.org/10.2478/s11772-012-0051-9
Litjens, G., Kooi, T., Bejnordi, B. E., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88. https://doi.org/10.1016/j.media.2017.07.00
M. M. Najafabadi, et all., ‘Deep learning applications and challenges in big data analytics’, J. Big Data, 2015, v. 2, no. 1, pp. 1–21, p.4. https://doi.org/10.1186/s40537-014-0007-7
Ma, J., & Yang, X. (2019, March). Automatic dental root CBCT image segmentation based on CNN and level set method. In Medical Imaging 2019: Image Processing (Vol. 10949, pp. 668-674). SPIE. https://doi.org/10.1117/12.2512359
Megalan Leo, L., & Kalpalatha Reddy, T. (2020). Dental caries classification system using deep learning based convolutional neural network. Journal of Computational and Theoretical Nanoscience, 17(9-10), 4660-4665.
Miki, Y., Muramatsu, C., Hayashi, T., Zhou, X., Hara, T., Katsumata, A., & Fujita, H. (2017). Classification of teeth in cone-beam CT using deep convolutional neural network. Computers in biology and medicine, 80, 24-29. https://doi.org/10.1016/j.compbiomed.2016.11.00
Mohammed, A., El-Antably, K., Zoair, M., Yasser, S. E., Hegazi, A., & El-Masry, N. (2022). An Approach Towards Vision Correction Display and Color blindness. MIUCC 2022 - 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference, 153–159. https://doi.org/10.1109/MIUCC55081.2022.9781710.
Moidu, N. P., Sharma, S., Chawla, A., Kumar, V., & Logani, A. (2022). Deep learning for categorization of endodontic lesion based on radiographic periapical index scoring system. Clinical Oral Investigations, 26(1), 651-658. https://doi.org/10.1007/s00784-021-04043-y
Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th International Conference on Machine Learning (ICML) (pp. 807-814).
Negm, A. S., Hassan, O. A., & Kandil, A. H. (2018). A decision support system for Acute Leukaemia classification based on digital microscopic images. Alexandria engineering journal, 57(4), 2319-2332. https://doi.org/10.1016/j.aej.2017.08.025
Okada, K., Rysavy, S., Flores, A., & Linguraru, M. G. (2015). Noninvasive differential diagnosis of dental periapical lesions in cone‐beam CT scans. Medical physics, 42(4), 1653-1665. https://doi.org/10.1118/1.4914418
Ozkan, Y., & Erdogmus, P. (2024). Evaluation of Classification Performance of New Layered Convolutional Neural Network Architecture on Offline Handwritten Signature Images. Symmetry, 16(6), 649. https://doi.org/10.3390/sym16060649
Patel, S., Brown, J., Pimentel, T., Kelly, R. D., Abella, F., & Durack, C. (2019). Cone beam computed tomography in Endodontics–a review of the literature. International endodontic journal, 52(8), 1138-1152. https://doi.org/10.1111/iej.13115
Prajapati, S. A., Nagaraj, R., & Mitra, S. (2017, August). Classification of dental diseases using CNN and transfer learning. In 2017 5th International Symposium on Computational and Business Intelligence (ISCBI) (pp. 70-74). IEEE. doi: 10.1109/ISCBI.2017.8053547.
Qiumei, Z., Dan, T., & Fenghua, W. (2019). Improved Convolutional Neural Network Based on Fast Exponentially Linear Unit Activation Function. IEEE Access, 7, 151359–151367. https://doi.org/10.1109/ACCESS.2019.2948112.
Rathee, D., & Mann, S. (2022). Daltonizer: A CNN-based Framework for Monochromatic and Dichromatic Color-Blindness. AIST 2022 - 4th International Conference on Artificial Intelligence and Speech Technology. https://doi.org/10.1109/AIST55798.2022.10065004.
Rajinikanth, V., Satapathy, S. C., Fernandes, S. L., & Nachiappan, S. (2017). Entropy based segmentation of tumor from brain MR images–a study with teaching learning based optimization. Pattern Recognition Letters, 94, 87-95. https://doi.org/10.1016/j.patrec.2017.05.028
Rasamoelina, A. D., Adjailia, F., & Sin˘c´ak, P. (2020). A Review of Activation Function for Artiﬁcial Neural Network. SAMI 2020 : IEEE 18th World Symposium on Applied Machine Intelligence and Informatics : Proceedings : January 23-25, 2020, Herl’any, Slovakia, 281–286. doi: 10.1109/SAMI48414.2020.9108717.
Rawat, J., Singh, A., Bhadauria, H. S., Virmani, J., & Devgun, J. S. (2017). Classification of acute lymphoblastic leukaemia using hybrid hierarchical classifiers. Multimedia Tools and Applications, 76(18), 19057-19085. https://doi.org/10.1007/s11042-017-4478-3
Rawat, W., & Wang, Z. (2017). Deep convolutional neural networks for image classification: A comprehensive review. Neural Computing and Applications, 30(8), 1185-1195. doi: 10.1162/neco_a_00990.
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 234-241. https://doi.org/10.1007/978-3-319-24574-4_28
S.Hayou et.all., “On the Impact of the Activation Function on Deep Neural Networks Training”, arXiv, 2019, pp.1-22, p.1.
S. H. Abbood, H. N. A. Hamed, M. S. M. Rahim, A. Rehman, T. Saba and S. A. Bahaj, "Hybrid Retinal Image Enhancement Algorithm for Diabetic Retinopathy Diagnostic Using Deep Learning Model," in IEEE Access, vol. 10, pp. 73079-73086, 2022, doi: 10.1109/ACCESS.2022.3189374.
Scherer, D., Müller, A., & Behnke, S. (2010). Evaluation of pooling operations in convolutional architectures for object recognition. In International Conference on Artificial Neural Networks (ICANN) (pp. 92-101). https://doi.org/10.1007/978-3-642-15825-4_10
Schwendicke, F., Golla, T., Dreher, M., & Krois, J. (2019). Convolutional neural networks for dental image diagnostics: A scoping review. Journal of dentistry, 91, 103226. https://doi.org/10.1016/j.jdent.2019.103226
Shen, S. L., Zhang, N., Zhou, A., & Yin, Z. Y. (2022). Enhancement of neural networks with an alternative activation function tanhLU. Expert Systems with Applications, 199, 117181. https://doi.org/10.1016/j.eswa.2022.117181
Sonavane, A., Yadav, R., & Khamparia, A. (2021). Dental cavity classification of using convolutional neural network. In IOP conference series: materials science and engineering (Vol. 1022, No. 1, p. 012116). IOP Publishing. DOI:10.1088/1757-899X/1022/1/012116
Srivastava, N., Hinton, G. E., Krizhevsky, A., et al. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929-1958.
V. Shankar, M. M. Deshpande, N. Chaitra and S. Aditi, "Automatic detection of acute lymphoblasitc leukemia using image processing," 2016 IEEE International Conference on Advances in Computer Applications (ICACA), Coimbatore, India, 2016, pp. 186-189, doi: 10.1109/ICACA.2016.7887948.
Vasdev, D., Gupta, V., Shubham, S., Chaudhary, A., Jain, N., Salimi, M., & Ahmadian, A. (2022). Periapical dental X-ray image classification using deep neural networks. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04961-4
Veena Divya K., A. Jatti, R. Joshi & Deepu Krishna S., "Characterization of dental pathologies using digital panoramic X-ray images based on texture analysis," 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Korea (South), 2017, pp. 592-595, doi: 10.1109/EMBC.2017.8036894.
Wang, X., Meng, X., & Yan, S. (2021). Deep Learning‐Based Image Segmentation of Cone‐Beam Computed Tomography Images for Oral Lesion Detection. Journal of Healthcare Engineering, 2021(1), 4603475. https://doi.org/10.1155/2021/4603475
Wang X, Ren H, Wang A. Smish: A Novel Activation Function for Deep Learning Methods. Electronics. 2022; 11(4):540. doi:doi.org/10.3390/electronics11040540.
Widodo, H. B., Soelaiman, A., Ramadhani, Y., & Supriyanti, R. (2016). Calculating contrast stretching variables in order to improve dental radiology image quality. In IOP Conference Series: Materials Science and Engineering (Vol. 105, No. 1, p. 012002). IOP Publishing. DOI:10.1088/1757-899X/105/1/012002
Y. Wang, Z. Yu & S. Li, "A Flexible Assistant Tool with Dynamic Scanning to Enhance the Ability of Color Discrimination," 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China, 2021, pp. 307-311, doi: 10.1109/ICAICA52286.2021.9497942.
Yadav, P. S., Gupta, B., & Lamba, S. S. (2024). A new approach of contrast enhancement for Medical Images based on entropy curve. Biomedical Signal Processing and Control, 88, 105625. https://doi.org/10.1016/j.bspc.2023.10562
Z. -Q. Zhao, P. Zheng, S. -T. Xu & X. Wu, "Object Detection With Deep Learning: A Review," in IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 11, pp. 3212-3232, Nov. 2019, doi: 10.1109/TNNLS.2018.2876865.
Zanini, L. G. K., Rubira-Bullen, I. R. F., & dos Santos Nunes, F. D. L. (2024). Enhancing dental caries classification in CBCT images by using image processing and self-supervised learning. Computers in Biology and Medicine, 183, 109221. https://doi.org/10.1016/j.compbiomed.2024.10922

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