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Related Experiment Video

Updated: Jul 27, 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

Brain Tumor Class Detection in Flair/T2 Modality MRI Slices Using Elephant-Herd Algorithm Optimized Features.

Venkatesan Rajinikanth¹, P M Durai Raj Vincent², C N Gnanaprakasam³

¹Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India.

Diagnostics (Basel, Switzerland)

|June 10, 2023

Summary

This summary is machine-generated.

This study introduces an efficient deep learning scheme for brain tumor detection in MRI scans. The integrated feature approach achieved 99.67% accuracy, demonstrating its reliability even with noisy data.

Keywords:

MRI slice brain-tumor classification classification deep learning feature optimization

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Science

Background:

Advancements in computing and technology have enabled automation in healthcare.
Accurate brain tumor detection is crucial for effective patient treatment.

Purpose of the Study:

To develop an efficient deep-learning-based scheme for brain tumor (BT) detection.
To detect tumors in FLAIR and T2-modality magnetic resonance imaging (MRI) slices.
To verify the scheme's reliability using clinically collected and benchmark MRI slices.

Main Methods:

Pre-processing raw MRI images.
Deep-feature extraction using pretrained models.
Watershed-algorithm-based BT segmentation and shape feature mining.
Feature optimization using the elephant-herding algorithm (EHA).
Binary classification and verification using three-fold cross-validation.

Main Results:

The integrated feature-based scheme achieved a classification accuracy of 99.6667% with a support-vector-machine (SVM) classifier.
The scheme demonstrated reliable performance on both benchmark (BRATS, TCIA) and clinically collected MRI slices.
The proposed method showed improved classification results even when tested on noise-attacked MRI slices.

Conclusions:

The developed deep-learning scheme offers an efficient and accurate method for brain tumor detection in MRI.
The integration of deep features, shape features, and EHA optimization enhances classification performance.
The scheme's robustness against noise suggests its potential for real-world clinical applications.