Automated brain tumor detection from magnetic resonance imaging using AI-PSO-based deep learning models

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-PSOBased Deep Learning Models for automated detection and diagnosis purposes.