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Automated Brain Tumor Detection From Magnetic Resonance Imaging Using AI-PSO-Based Deep Learning Models

Automated Brain Tumor Detection From Magnetic Resonance Imaging Using AI-PSO-Based Deep Learning Models
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Author(s): Nilamadhab Mishra (VIT Bhopal University, India), Abhishek Mamidi (VIT Bhopal University, India), Saroja Kumar Rout (Vardhaman College of Engineering, Hyderabad, India), Amit Thakur (VIT Bhopal University, India)and Meshal Alharbi (Prince Sattam Bin Abdulaziz University, Saudi Arabia)
Copyright: 2025
Pages: 26
Source title: Enhancing Automated Decision-Making Through AI
Source Author(s)/Editor(s): Shalin Hai-Jew (Sedgwick County, USA)
DOI: 10.4018/979-8-3693-6230-3.ch009

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Abstract

The proposed methodology begins with optimizing deep CNN parameters using Particle Swarm Optimization (PSO) to find an optimal configuration that maximizes the network's performance. PSO aids in the exploration of the high-dimensional parameter space, optimizing CNN's convolutional layers for feature extraction. Subsequently, the CNN is employed to automatically extract hierarchical features from magnetic resonance imaging (MRI) scans, capturing intricate patterns indicative of automated brain tumor detection. Healthcare practitioners can use the AI-PSO-Based Deep Learning Models for automated detection and diagnosis purposes.

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