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Evaluating VGG16, ResNet50, and EfficientNet-B0 for Brain Tumor MRI Classification: A Two-Phase Transfer Learning Approach
ABOUT
This conference paper compares VGG16, ResNet50, and EfficientNet-B0 for multi-class brain tumor classification from MRI images. It uses a two-phase transfer-learning approach to evaluate how the three convolutional neural network architectures perform on the same classification task.
PUBLICATION INFO
Venue2026 International Seminar on Intelligent Business and Edge-Computing Research (ISIBER)
DateFebruary 2026
TypeConference Paper
DOI10.1109/ISIBER68248.2026.11469939
AUTHORS
F. A. Damastuti, A. B. Gumelar, K. Firmansyah
SUMMARY
Comparison of three convolutional neural network architectures for multi-class brain tumor MRI classification using a two-phase transfer-learning approach.