# MRI Brain Tumor Detection with Computer Vision

Source: [MRI Brain Tumor Detection with Computer Vision](<https://www.sota2.com/research/paper/68fa7c091e209131cea8c01c>)

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

Data updated: 2025-10-28T13:57:23.083Z

Authors: Jack Krolik, Jake Lynn, John Henry Rudden, Dmytro Vremenko

Published: 2025-10-11T15:07:52.000Z

## Abstract

This study explores the application of deep learning techniques in the automated detection and segmentation of brain tumors from MRI scans. We employ several machine learning models, including basic logistic regression, Convolutional Neural Networks (CNNs), and Residual Networks (ResNet) to classify brain tumors effectively. Additionally, we investigate the use of U-Net for semantic segmentation and EfficientDet for anchor-based object detection to enhance the localization and identification of tumors. Our results demonstrate promising improvements in the accuracy and efficiency of brain tumor diagnostics, underscoring the potential of deep learning in medical imaging and its significance in improving clinical outcomes.

## 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/2510.10250>)

[PDF](<https://arxiv.org/pdf/2510.10250.pdf>)