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

Source: [Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images](<https://www.sota2.com/research/paper/68fa744f1e209131cea60f65>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:06:43.001Z

Authors: Xiaoyi Liu, Zhuoyue Wang

Published: 2024-08-01T15:20:20.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2408.00636>)

[PDF](<https://arxiv.org/pdf/2408.00636.pdf>)