Comparative Evaluation of Transfer Learning for Classification of Brain
  Tumor Using MRI
Abu Kaisar Mohammad Masum Nusrat Badhon S. M. Saiful Islam Badhon Nushrat Jahan Ria Sheikh Abujar Muntaser Mansur Syed Naveed Mahmud
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 categorize 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.