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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

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Enhanced Brain Tumor Classification Through Optimized Semantic Preserved Generative Adversarial Networks.

Durbhakula M K Chaitanya¹, Srilakshmi Aouthu¹, Narra Dhanalakshmi²

¹Department of Electronics and Communication Engineering, Vasavi College of Engineering, Hyderabad, Telangana, India.

Microscopy Research and Technique

|December 16, 2024

Summary

This study introduces an advanced brain tumor classification (BTC) method using Semantic-Preserved Generative Adversarial Networks (SPGAN) optimized with Hunger Games Search Optimization (HGSO). The novel approach achieves high accuracy in diagnosing Glioma, Meningioma, and Pituitary tumors.

Keywords:

Hunger Games Search Optimization Semantic‐Preserved Generative Adversarial Network brain tumor MRI dataset quaternion offset linear canonical transform

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Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Accurate and timely brain tumor classification (BTC) is crucial for effective treatment.
Traditional manual analysis methods for BTC are time-consuming and prone to errors.
Developing automated, high-accuracy BTC systems is a significant challenge in medical diagnostics.

Purpose of the Study:

To propose an automated and highly accurate brain tumor classification method.
To enhance the diagnostic performance for Glioma, Meningioma, and Pituitary tumors.
To improve upon existing brain tumor classification models through advanced AI techniques.

Main Methods:

Utilized a Brain Tumor dataset for image analysis.
Applied trust-based distributed set-membership filtering (TDSF) for noise reduction.
Employed quaternion offset linear canonical transform (QOLCT) for feature extraction (Grayscale statistics and Haralick textures).
Implemented a Semantic-Preserved Generative Adversarial Network (SPGAN) for tumor classification.
Optimized SPGAN weights using Hunger Games Search Optimization (HGSO).

Main Results:

Achieved high classification accuracies: 99.72% for Glioma, 99.65% for Meningioma, and 99.52% for Pituitary.
Obtained low Mean Squared Error (MSE) values: 0.45% (Glioma), 0.39% (Meningioma), and 0.5% (Pituitary).
Demonstrated superior performance compared to existing brain tumor classification models.

Conclusions:

The proposed BTC-SPGAN-HGSO method significantly improves the accuracy of brain tumor classification.
The approach offers a reliable tool to assist neurologists and physicians in making precise diagnostic decisions.
The study highlights the potential of integrating advanced AI models for enhanced medical image analysis.