Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images

TL;DR

This paper explores the use of five deep learning models, including four pre-trained and one novel, to classify MRI images into different brain tumor categories, aiming to improve diagnostic accuracy.

Contribution

The study introduces a new MobileNet-BT model and compares it with existing pre-trained models for brain tumor classification from MRI images.

Findings

MobileNet-BT achieves competitive accuracy.

Pre-trained models perform well on MRI classification.

The proposed model enhances classification performance.

Abstract

Brain tumors are among the deadliest diseases in the world. Magnetic Resonance Imaging (MRI) is one of the most effective ways to detect brain tumors. Accurate detection of brain tumors based on MRI scans is critical, as it can potentially save many lives and facilitate better decision-making at the early stages of the disease. Within our paper, four different types of MRI-based images have been collected from the database: glioma tumor, no tumor, pituitary tumor, and meningioma tumor. Our study focuses on making predictions for brain tumor classification. Five models, including four pre-trained models (MobileNet, EfficientNet-B0, ResNet-18, and VGG16) and one new model, MobileNet-BT, have been proposed for this study.