Hybrid Models of Multi-CNN Features with ACO Algorithm for MRI Analysis for Early Detection of Multiple Sclerosis

Multiple Sclerosis (MS) poses significant health risks. Patients may face neurodegeneration, mobility issues, cognitive decline, and a reduced quality of life. Manual diagnosis by neurologists is prone to limitations, making AI-based classification crucial for early detection. Therefore, automated classification using Artificial Intelligence (AI) techniques has a crucial role in addressing the limitations of manual classification and preventing the development of MS to advanced stages. This study developed hybrid systems integrating XGBoost (eXtreme Gradient Boosting) with multi-CNN (Convolutional Neural Networks) features based on Ant Colony Optimization (ACO) and Maximum Entropy Score-based Selection (MESbS) algorithms for early classification of MRI (Magnetic Resonance Imaging) images in a multi-class and binary-class MS dataset. All hybrid systems started by enhancing MRI images using the fusion processes of a Gaussian filter and Contrast-Limited Adaptive Histogram Equalization (CLAHE). Then, the Gradient Vector Flow (GVF) algorithm was applied to select white matter (regions of interest) within the brain and segment them from the surrounding brain structures. These regions of interest were processed by CNN models (ResNet101, DenseNet201, and MobileNet) to extract deep feature maps, which were then combined into fused feature vectors of multi-CNN model combinations (ResNet101-DenseNet201, DenseNet201-MobileNet, ResNet101-MobileNet, and ResNet101-DenseNet201-MobileNet). The multi-CNN features underwent dimensionality reduction using ACO and MESbS algorithms to remove unimportant features and retain important features. The XGBoost classifier employed the resultant feature vectors for classification. All developed hybrid systems displayed promising outcomes. For multi-class classification, the XGBoost model using ResNet101-DenseNet201-MobileNet features selected by ACO attained 99.4% accuracy, 99.45% precision, and 99.75% specificity, surpassing prior studies (93.76% accuracy). It reached 99.6% accuracy, 99.65% precision, and 99.55% specificity in binary-class classification. These results demonstrate the effectiveness of multi-CNN fusion with feature selection in improving MS classification accuracy.