Gelişmiş Ağız Kanseri Tanısı için Son Teknoloji VGG Mimarilerinden Yararlanan Hibrit Derin Öğrenme Stratejileri

Abstract

Ağız Kanseri (OC), dünya çapında giderek artan yaygınlığı ve geç teşhis edildiğinde yüksek ölüm oranı ile kritik bir halk sağlığı sorunu haline gelmiştir. Tütün ve alkol kullanımı, Human Papilloma Virüsü (HPV) enfeksiyonları ve çeşitli çevresel faktörler hastalığın gelişiminde önemli rol oynar. Hastalığın erken teşhisi, tedavi başarısını ve yaşam kalitesini önemli ölçüde artırmaktadır. Ancak, geleneksel klinik muayeneler ve manuel değerlendirme yöntemleri hem zaman alıcıdır hem de uzman bağımlılığı nedeniyle yüksek yanlış sınıflandırma oranlarına yol açabilir. Bu nedenle, bu çalışmada, OC'nin otomatik sınıflandırılması için derin öğrenme tabanlı bir hibrit yaklaşım önerilmiştir. Önerilen model, OC görüntülerinden derin özellikler çıkarmak için Görsel Geometri Grubu (VGG) mimarisinin VGG11, VGG13, VGG16 ve VGG19 farklı varyantlarını kullanılmıştır. Elde edilen derin özellikler, Extreme Gradient Boosting (XGBoost), k-En Yakın Komşu (kNN), Destek Vektör Makineleri (SVM), Rastgele Orman (RF) ve Light Gradient Boosting Machine (LGBM) dahil olmak üzere çeşitli sınıflandırıcılarla işlenmiştir ve kapsamlı bir deneysel analiz gerçekleştirilmiştir. Deneysel bulgular, özellikle VGG19+SVM hibrit modelinin üstün performans gösterdiğini ve sınıflar arası ayrımcılık için en yüksek AUC puanını (0,9144) elde ettiğini göstermektedir. Ayrıca, VGG19+LGBM modeli en yüksek doğruluk (0,9158) oranını elde ederek güçlü sınıflandırma performansı göstermiştir. Sonuçlar, VGG tabanlı derin özellik çıkarmanın OC sınıflandırmasında yüksek doğruluk ve güçlü ayrımcılık sağladığını göstermektedir. Bu bulgular, önerilen hibrit yaklaşımın klinik karar destek sistemlerinde etkili bir şekilde kullanılabilecek güvenilir bir tanı aracı olduğunu göstermektedir.

Keywords

• Ağız kanseri tespiti
• VGG
• Derin öğrenme
• Transfer öğrenme

Hybrid Deep Learning Strategies Leveraging Cutting-Edge VGG Architectures for Advanced Oral Cancer Diagnosis

Abstract

Oral Cancer (OC) has become a critical public health problem, with its increasing prevalence worldwide and high mortality rate when diagnosed late. Tobacco and alcohol use, Human Papilloma Virus (HPV) infections, and various environmental factors play a significant role in the development of the disease. Early detection of the disease significantly improves treatment success and quality of life. However, traditional clinical examinations and manual assessment methods are both time-consuming and can lead to high misclassification rates due to expert dependency. In this study, a deep learning-based hybrid approach for the automatic classification of OC is proposed. The proposed model utilizes different variants of the Visual Geometry Group (VGG) architecture, namely VGG11, VGG13, VGG16, and VGG19, to extract deep features from OC images. The resulting deep features were processed with various classifiers, including Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (kNN), Support Vector Machines (SVM), Random Forest (RF), and Light Gradient Boosting Machine (LGBM), and a comprehensive experimental analysis was conducted. Experimental findings demonstrate that the VGG19+SVM hybrid model, in particular, demonstrated superior performance, achieving the highest AUC score (0.9144) for inter-class discrimination. Furthermore, the VGG19+LGBM model achieved the highest accuracy rate (0.9158), demonstrating strong classification performance. The results demonstrate that VGG-based deep feature extraction provides high accuracy and strong discrimination in OC classification. These findings demonstrate that the proposed hybrid approach is a reliable diagnostic tool that can be effectively used in clinical decision support systems.

Keywords

• Oral cancer detection
• VGG
• deep learning
• transfer learning

References

1. Alosaimi, W., & Uddin, M. I. (2022). Efficient data augmentation techniques for improved classification in limited dataset of oral squamous cell carcinoma. Computer Modeling in Engineering & Sciences, 131, 1387–1401.
2. Chang, X., Yu, M., Liu, R., Jing, R., Ding, J., Xia, J., Zhu, Z., Li, X., Yao, Q., Zhu, L., & Zhang, T. (2023). Deep learning methods for oral cancer detection using Raman spectroscopy. Vibrational Spectroscopy, 126, 103522. https://doi.org/10.1016/j.vibspec.2023.103522
3. Das, M., Dash, R., & Mishra, S. K. (2023). Automatic detection of oral squamous cell carcinoma from histopathological images of oral mucosa using deep convolutional neural network. International Journal of Environmental Research and Public Health, 20(3), 2131. https://doi.org/10.3390/ijerph20032131
4. Fu, Q., Chen, Y., Li, Z., Jing, Q., Hu, C., Liu, H., ... & Xiong, X. (2020). A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study. EClinicalMedicine, 27, 100558. https://doi.org/10.1016/j.eclinm.2020.100558
5. Gomes, R. F. T., Schmith, J., Figueiredo, R. M. D., Freitas, S. A., Machado, G. N., Romanini, J., & Carrard, V. C. (2023). Use of artificial intelligence in the classification of elementary oral lesions from clinical images. International journal of environmental research and public health, 20(5), 3894.
6. Huang, Q., Ding, H., & Razmjooy, N. (2023). Optimal deep learning neural network using ISSA for diagnosing the oral cancer. Biomedical Signal Processing and Control, 84, 104749. https://doi.org/10.1016/j.bspc.2023.104749
7. Huang, Q., Ding, H., & Razmjooy, N. (2024). Oral cancer detection using convolutional neural network optimized by combined seagull optimization algorithm. Biomedical Signal Processing and Control, 87, 105546. https://doi.org/10.1016/j.bspc.2023.105546
8. Kabir, M. F., Ahmad, M. Y., Uddin, R., Cordero, M., & Kant, S. (2025). Accurate and lightweight oral cancer detection using SE-MobileViT on clinically validated image dataset. Discover Artificial Intelligence, 5, 173. https://doi.org/10.1007/s44163-025-00442-2

9. Kaya, V., & Akgül, İ. (2023). Classification of skin cancer using VGGNet model structures. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 13(1), 190-198. https://doi.org/10.17714/gumusfenbil.1069894
10. Li, L., Pu, C., Tao, J., Zhu, L., Hu, S., Qiao, B., Xing, L., Wei, B., Shi, C., Chen, P., & Zhang, H. (2024). Development of an oral cancer detection system through deep learning. BMC Oral Health, 24, 1468. https://doi.org/10.1186/s12903-024-05195-5
11. Liu, P., & Bagi, K. (2025). A tailored deep learning approach for early detection of oral cancer using a 19-layer CNN on clinical lip and tongue images. Scientific Reports, 15, 23851. https://doi.org/10.1038/s41598-025-07957-9
12. Myriam, H., Abdelhamid, A. A., El-Kenawy, E. S. M., Ibrahim, A., & others. (2023). Advanced meta-heuristic algorithm based on particle swarm and Al-Biruni Earth radius optimization methods for oral cancer detection. IEEE Access, 11, 23681–23700. https://doi.org/10.1109/ACCESS.2023.3253430
13. Prabhakaran, R., & Mohana, J. (2020). Detection of oral cancer using machine learning classification methods. International Journal of Electrical Engineering and Technology, 11(3), 384–393. https://ssrn.com/abstract=3638829
14. Shetty, S., & Patil, A. (2023). Duck pack optimization with deep transfer learning-enabled oral squamous cell carcinoma classification on histopathological images. International Journal of Grid and High Performance Computing, 15, 1–21.
15. Soni, A., Sethy, P. K., Dewangan, A. K., Nanthaamornphong, A., Behera, S. K., & Devi, B. (2024). Enhancing oral squamous cell carcinoma detection: A novel approach using improved EfficientNet architecture. BMC Oral Health, 24, 601
16. Shah, S. J. H., Albishri, A., Wang, R., & Lee, Y. (2025). Integrating local and global attention mechanisms for enhanced oral cancer detection and explainability. Computers in Biology and Medicine, 189, 109841. https://doi.org/10.1016/j.compbiomed.2025.109841
17. Warin, K., & Suebnukarn, S. (2024). Deep learning in oral cancer: A systematic review. BMC Oral Health, 24, 212. https://doi.org/10.1186/s12903-024-03993-5
18. Warin, K., Limprasert, W., Suebnukarn, S., Jinaporntham, S., & Jantana, P. (2021). Automatic classification and detection of oral cancer in photographic images using deep learning algorithms. Journal of Oral Pathology & Medicine, 50(9), 911–918. https://doi.org/10.1111/jop.13227
19. Welikala, R. A., Remagnino, P., Lim, J. H., Chan, C. S., Rajendran, S., Kallarakkal, T. G., Zain, R. B., Jayasinghe, R. D., Rimal, J., Kerr, A. R., Amtha, R., Patil, K., Tilakaratne, W. M., Gibson, J., Cheong, S. C., & Barman, S. A. (2020). Automated detection and classification of oral lesions using deep learning for early detection of oral cancer. IEEE Access, 8, 132677–132693. https://doi.org/10.1109/ACCESS.2020.3010180
20. Yaduvanshi, V., Murugan, R., & Goel, T. (2025). Automatic oral cancer detection and classification using modified local texture descriptor and machine learning algorithms. Multimedia Tools and Applications, 84(2), 1031–1055. https://doi.org/10.1007/s11042-024-19040-y
21. ZaidPy. (2022). Oral Cancer Dataset [Data set]. Kaggle. https://www.kaggle.com/datasets/zaidpy/oral-cancer-dataset