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

Öz

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.

Anahtar Kelimeler

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

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

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