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Dergiler / International Journal of Pure and Applied Sciences / 2026 / Volume: 12 Issue: 1

Improving CNN-based brain tumor classification via differential learning rate and data augmentation strategies

Özlem Akboyraz(MALATYA TURGUT ÖZAL ÜNİVERSİTESİ)0009-0007-8960-9769
Soner Kızıloluk(MALATYA TURGUT ÖZAL ÜNİVERSİTESİ)0000-0002-0381-9631
Sayfa
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Yayın Tarihi
2026-06-22
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

EfficientNetB0 achieved the highest performance, with 98.78% accuracy and an F1-score of 0.9878.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Diagnosing brain tumors accurately at an early stage plays an important role in treatment planning and patient survival.
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Temel Bulgular & Veri:This study evaluates five transfer learning-based convolutional neural network models, namely DenseNet121, EfficientNetB0, InceptionV3, MobileNetV2, and ResNet50, for multi-class brain tumor classification.
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Sonuç & Katkı:EfficientNetB0 achieved the highest performance, with 98.78% accuracy and an F1-score of 0.9878.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Brain tumor classification · Deep learning · Differential learning rate

Abstract

Diagnosing brain tumors accurately at an early stage plays an important role in treatment planning and patient survival. Since manual interpretation of medical images is time-consuming and depends heavily on expert experience, automated methods have become increasingly valuable. This study evaluates five transfer learning-based convolutional neural network models, namely DenseNet121, EfficientNetB0, InceptionV3, MobileNetV2, and ResNet50, for multi-class brain tumor classification. To improve classification performance, several data augmentation techniques, including brightness and contrast adjustment, blurring, flipping, and rotation, were applied together with a differential learning rate strategy. This strategy allowed the lower layers of the pretrained networks to preserve general visual representations, while the upper layers adapted more effectively to tumor-specific patterns. The results show that this training approach improved all evaluation metrics compared with fixed learning rate training. EfficientNetB0 achieved the highest performance, with 98.78% accuracy and an F1-score of 0.9878.

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Bu Makaleye Atıf Yap

Akboyraz, Ö., Kızıloluk, S. (2026). Improving CNN-based brain tumor classification via differential learning rate and data augmentation strategies. International Journal of Pure and Applied Sciences, Volume: 12 Issue: 1. https://doi.org/10.29132/ijpas.1922851

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