Comparative Evaluation of Transfer Learning for Classification of Brain Tumor Using MRI

TL;DR

This study evaluates four transfer learning models for classifying three types of brain tumors in MRI images, highlighting ResNet-50's superior accuracy of 99.06% on a large benchmark dataset.

Contribution

It compares multiple transfer learning techniques for brain tumor classification and emphasizes the importance of dataset balance for improved accuracy.

Findings

ResNet-50 achieved 99.06% accuracy.

Balanced datasets significantly enhance model performance.

Transfer learning outperforms traditional classification algorithms.

Abstract

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.