Abnormal growth of cells in the brain and its
surrounding tissues is known as a brain tumor. There are two
types, one is benign (non-cancerous) and another is malignant
(cancerous) which may cause death. The radiologists’ ability to
diagnose malignancies is greatly aided by magnetic resonance
imaging (MRI). Brain cancer diagnosis has been considerably
expedited by the field of computer-assisted diagnostics, especially
in machine learning and deep learning. In our study, we cate-
gorize three different kinds of brain tumors using four transfer
learning techniques. Our models were tested on a benchmark
dataset of 3064 MRI pictures representing three different forms
of brain cancer. Notably, ResNet-50 outperformed other models
with a remarkable accuracy of 99.06%. We stress the significance
of a balanced dataset for improving accuracy without the
use of augmentation methods. Additionally, we experimentally
demonstrate our method and compare with other classification
algorithms on the CE-MRI dataset using evaluations like F1-
score, AUC, precision and recall.
Index Terms—Transfer Learning, MRI, Brain Cancer.