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WBM-DLNets: Wrapper-Based Metaheuristic Deep Learning Networks Feature Optimization for Enhancing Brain Tumor

Muhammad Umair Ali¹, Shaik Javeed Hussain², Amad Zafar¹

¹Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea.

Bioengineering (Basel, Switzerland)

|April 28, 2023

Summary

This study introduces wrapper-based metaheuristic deep learning networks (WBM-DLNets) for brain tumor diagnosis. Feature optimization significantly improved classification accuracy for magnetic resonance imaging analysis.

Keywords:

brain MRI brain tumor detection deep learning networks image processing wrapper-based metaheuristic algorithms

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Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Brain tumor diagnosis relies heavily on accurate interpretation of magnetic resonance imaging (MRI).
Traditional diagnostic methods can be time-consuming and prone to inter-observer variability.
Deep learning (DL) offers potential for automated analysis, but feature selection remains a challenge.

Purpose of the Study:

To develop and validate a novel feature optimization algorithm for brain tumor diagnosis using MRI.
To enhance classification accuracy by integrating metaheuristic optimization with deep learning networks.
To compare the performance of the proposed method against existing approaches.

Main Methods:

Utilized 16 pretrained deep learning networks to extract features from MRI data.
Employed eight metaheuristic optimization algorithms (e.g., marine predator algorithm, grey wolf optimization algorithm) to select optimal features.
Implemented a support vector machine (SVM) classifier with a cost function for performance evaluation.
Developed a deep learning network selection strategy and concatenated features from the best-performing networks.

Main Results:

The wrapper-based metaheuristic deep learning networks (WBM-DLNets) approach significantly improved classification accuracy compared to using full deep features.
DenseNet-201 with grey wolf optimization algorithm (GWOA) and EfficientNet-b0 with atom search optimization algorithm (ASOA) achieved the highest accuracy of 95.7%.
The proposed WBM-DLNets method demonstrated superior performance on an online dataset.

Conclusions:

Feature optimization using WBM-DLNets is a highly effective strategy for improving brain tumor classification accuracy in MRI.
Metaheuristic algorithms combined with deep learning provide a powerful framework for medical image analysis.
The developed approach offers a promising tool for enhancing the accuracy and efficiency of brain tumor diagnosis.