Gelişmiş Ağız Kanseri Tanısı İçin Önceden Eğitilmiş Derin Sinir Ağlarının Geleneksel Sınıflandırma Teknikleriyle Entegre Edilmesi

Year 2025, Volume: 13 Issue: 1, 26 - 36, 30.06.2025

Bilal Şenol , Uğur Demiroğlu

https://doi.org/10.18586/msufbd.1631254

Abstract

Bu araştırmanın amacı, ağız kanseri görüntülerinin sınıflandırılmasına yönelik hibrit bir yaklaşım sunmaktır. Bu yaklaşım, Destek Vektör Makineleri (SVM), K-En Yakın Komşular (KNN) ve Karar Ağaçları gibi geleneksel sınıflandırma yöntemlerini, önceden eğitilmiş derin sinir ağlarından (GoogleNet ve MobileNetV2) gelişmiş özellik çıkarma ile birleştirir. Önerilen yöntemin kullanımıyla, özellikler derin öğrenme modellerinden çıkarılır ve bu da tanı doğruluğunu artıran sağlam bir hibrit modelin oluşturulmasıyla sonuçlanır. Hibrit model, Kuadratik SVM ile %90,01'lik bir sınıflandırma doğruluğuna ulaşır; bu da tekil derin öğrenme modellerine göre %22,36'lık bir iyileştirmeyi temsil eder. Karşılaştırmalı analizler, hibrit modelin elde ettiği muazzam performans avantajlarını göstermektedir. Bulgular, özellikle ağız kanseri için tanı sürecinde tıbbi resim kategorizasyonunun doğruluğunu ve güvenilirliğini artırmak için çağdaş derin öğrenme becerilerinin eski yöntemlerle birleştirilmesinin potansiyelini vurgulamaktadır.

Keywords

Ağız Kanseri , Görüntü Sınıflandırma , Hibrit Model , GoogleNet , MobileNet-v2

Integrating Pretrained Deep Neural Networks with Traditional Classification Techniques for Enhanced Oral Cancer Diagnosis

Abstract

The purpose of this research is to present a hybrid approach to the classification of oral cancer images. This approach combines traditional classification methods such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Decision Trees with advanced feature extraction from pretrained deep neural networks (GoogleNet and MobileNetV2). Through the use of the suggested method, features are extracted from the deep learning models, resulting in the formation of a robust hybrid model that enhances diagnostic accuracy. The hybrid model achieves a classification accuracy of 90.01% with Quadratic SVM, which represents a 22.36% improvement over solo deep learning models. Comparative analyses indicate the tremendous performance advantages that the hybrid model has achieved. The findings highlight the potential of merging contemporary deep learning skills with older methods in order to improve the accuracy and dependability of medical picture categorization, particularly in the diagnostic process for oral cancer.

Keywords

Oral Cancer , Image Classification , Hybrid Model , GoogleNet , MobileNet-v2

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