MRI Image Classification of Brain Tumors Using VGG16-Based Transfer Learning and Data Augmentation as a Medical Diagnosis Support System
DOI:
10.29303/jppipa.v12i1.14218Published:
2026-01-25Downloads
Abstract
Brain tumors are diseases that require early detection and accurate diagnosis. Various studies have applied deep learning methods to classify MRI images of brain tumors, but they still face dataset limitations and imbalanced class distributions that impact model performance. This study aims to evaluate the performance of the transfer learning-based VGG16 model in classifying brain tumors using MRI images. The study used 7,023 MRI images, including glioma, meningioma, pituitary, and no tumor, with a balanced training data distribution. Pre-processing included resizing, data splitting, and augmentation in the form of rotation, width shift, height shift, and zoom to increase data diversity and reduce the impact of class imbalance. The model was trained using several training-validation data splits (70:30, 80:20, and 90:10) with variations of the Adam, RMSprop, and AdamW optimizers and learning rates between 0.1 and 0.0001. The best configuration was obtained in the 80:20 scenario with the Adam optimizer and a learning rate of 0.0001, which was used in the final testing stage using test data that were never used during training and validation. The results showed the highest validation accuracy of 99.89% and a testing accuracy of 98.00%. Confusion matrix analysis showed that all classes could be classified well without prediction bias.
Keywords:
Brain Tumor Data Augmentation MRI Transfer Learning VGG16References
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