Beyin Tümörü Sınıflandırması için Görü Dönüştürücü ve Transfer Öğrenmenin Karşılaştırmalı Analizi

Year 2025, Volume: 13 Issue: 1, 558 - 572, 30.01.2025

Ahmet Solak

https://doi.org/10.29130/dubited.1521340

Abstract

Beyin tümörlerinin doğru sınıflandırılması, nöro-onkolojide tedavi planlarını yönlendirmek ve hasta sonuçlarını iyileştirmek için kritik öneme sahiptir. Bu çalışmada, Manyetik Rezonans (MR) görüntüleri kullanılarak Vision Transformers (ViTs) yönteminin beyin tümörlerinin ikili sınıflandırmasındaki etkinliği araştırılmış ve VGG16, VGG19 ve ResNet50 gibi CNN tabanlı modellerle karşılaştırılmıştır. Doğruluk, kesinlik, duyarlılık ve F1-skoru gibi kapsamlı değerlendirme metrikleri, ViTs’in üstün performansını ortaya koymuştur; ViTs, %92,59 doğrulukla VGG16 (%85,19), VGG19 (%74,04) ve ResNet50'yi (%88,89) geride bırakmıştır. Bu bulgular, ViTs’in nöro-onkolojide tanısal doğruluğu artıran ve hasta bakımını iyileştiren dönüştürücü bir araç olarak klinik uygulamalara entegrasyonu için umut vadeden bir yöntem olduğunu göstermektedir.

Keywords

Beyin tümörü sınıflandırması, Görü dönüştürücü, Derin öğrenme, Transfer öğrenme, Tıbbi görüntüleme

A Comparative Analysis of Vision Transformers and Transfer Learning for Brain Tumor Classification

Abstract

Accurate brain tumor classification is crucial in neuro-oncology for guiding treatment plans and improving patient outcomes. Leveraging the potential of Vision Transformers (ViTs), this study investigates their efficacy in binary classification of brain tumors using magnetic resonance (MR) images, comparing them to CNN-based models such as VGG16, VGG19, and ResNet50. Comprehensive evaluation using accuracy, precision, recall, and F1-score reveals ViTs’ superior performance, achieving 92.59% accuracy, surpassing VGG16 (85.19%), VGG19 (74.04%), and ResNet50 (88.89%). These findings highlight ViTs as a transformative tool for clinical adoption, enhancing diagnostic accuracy and patient care in neuro-oncology.

Keywords

Brain tumor classification, Vision transformers, Deep learning, Transfer learning, Medical imaging

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