DOI: 10.29132/ijpas.1922851 ISSN: 2149-0910

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

Özlem Akboyraz, Soner Kızıloluk
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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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