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Related Experiment Video

Updated: Jun 6, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

Automated brain tumor recognition using equilibrium optimizer with deep learning approach on MRI images.

Mahmoud Ragab¹, Iyad Katib², Sanaa A Sharaf²

¹Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia. mragab@kau.edu.sa.

Scientific Reports

|November 28, 2024

Summary

This study introduces a novel AI approach for brain tumor recognition in MRI scans. The BTR-EODLA technique achieves 98.78% accuracy, improving diagnostic capabilities.

Keywords:

Brain tumor recognition Equilibrium optimizer Image processing Magnetic resonance imaging Median filtering Stacked auto encoder

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Area of Science:

Medical Imaging
Artificial Intelligence
Deep Learning

Background:

Brain tumors (BT) pose significant health risks due to their location.
Artificial intelligence (AI), including deep learning (DL) and machine learning (ML), offers advanced tools for disease diagnosis and treatment.
AI analyzes brain Magnetic Resonance Imaging (MRI) to identify and categorize tumors, aiding medical professionals in diagnosis and treatment planning.

Purpose of the Study:

To develop and validate a novel AI technique for accurate brain tumor recognition in MRI images.
To enhance the early detection and classification of brain tumors using deep learning and optimization algorithms.

Main Methods:

The Brain Tumor Recognition using an Equilibrium Optimizer with a Deep Learning Approach (BTR-EODLA) technique was employed.
Median filtering (MF) was used for noise reduction in MRI images.
Squeeze-excitation ResNet (SE-ResNet50) extracted features, optimized by the Equilibrium Optimizer (EO), and a stacked autoencoder (SAE) performed tumor detection.

Main Results:

The BTR-EODLA technique demonstrated high performance in brain tumor recognition.
Experimental validation showed a superior accuracy of 98.78% compared to existing models.
The method effectively identifies the presence of brain tumors in MRI scans.

Conclusions:

The BTR-EODLA technique offers a highly accurate and effective solution for brain tumor detection using AI and deep learning on MRI data.
This AI-driven approach has the potential to significantly improve diagnostic accuracy and patient management in neuro-oncology.