Integrating feature fusion with hybrid optimization for multiple sclerosis MRI classification

Detecting Multiple Sclerosis (MS) has previously been difficult to identify with MRI scans due to the subtlety and dispersion of lesions, as well as other imaging anomalies. The researchers in this study created a unique hybrid framework that combines two state-of-the-art convolutional neural networks, ResNet-50 and EfficientNet-B7. Additionally, a new hybrid bio-inspired optimisation strategy combining the Grey Wolf Optimiser (GWO) and the Genetic Algorithm (GA) is described. The technique simplifies the computations and ensures that the best characteristics are picked. We extracted deep features from both CNNs, used Principal Component Analysis (PCA) to reduce them to high dimensions, and then employed the GA-GWO method to identify suitable features. The enhanced artificial neural network (ANN) classifier outperformed standalone CNN-based models, achieving a maximum accuracy of 90.67%(refer to table 4). The suggested framework for dependable and comprehensible precision and processing efficacy. This work has the potential to motivate future studies in related areas.