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Magnetic Resonance Imaging

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Mri-based Meningioma Firmness Classification Using An Adversarial Feature Learning Approach.

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Mri-based Meningioma Firmness Classification Using An Adversarial Feature Learning Approach.

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MRI-Based Meningioma Firmness Classification Using an Adversarial Feature Learning Approach.

Miada Murad¹, Ameur Touir¹, Mohamed Maher Ben Ismail¹

¹Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia.

Sensors (Basel, Switzerland)

|March 17, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a new machine learning method for classifying meningioma firmness from MRI scans. The novel approach enhances diagnostic accuracy, improving surgical planning for patients.

Keywords:

convolutional neural network deep learning magnetic resonance images meningioma firmness detection

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Area of Science:

Neurosurgery
Medical Imaging
Artificial Intelligence

Background:

Meningioma firmness is crucial for surgical planning, but current MRI assessment methods are subjective and time-consuming.
Machine learning offers potential for objective classification, but often relies on manual feature engineering.

Purpose of the Study:

To develop and evaluate a novel adversarial feature learning approach for accurate meningioma firmness classification using MRI.
To improve upon existing machine learning methods for meningioma consistency classification.

Main Methods:

Utilized Bidirectional Generative Adversarial Network (BiGAN) for unsupervised feature extraction from MRI scans.
Developed a depth-wise separable deep learning model to map extracted MRI features to meningioma firmness classes (firm or soft).

Main Results:

The combined BiGAN encoder and depth-wise separable model significantly enhanced classification performance.
The proposed model achieved a high accuracy of 94.7% and a weighted F1-score of 95.0% in meningioma firmness classification.
Outperformed existing state-of-the-art methods in classifying meningioma consistency.

Conclusions:

The novel adversarial feature learning approach effectively extracts discriminative MRI features for meningioma firmness classification.
This method offers a more accurate and objective alternative to conventional subjective assessments, aiding surgical decision-making.