Deep Learning for Differentiating Active and Inactive Multiple Sclerosis Plaques: A Comparative Analysis of MRI-Based Classification Models

Multiple sclerosis (MS) is a chronic inflammatory disease-causing neurological disability, particularly in young adults. Magnetic resonance imaging (MRI) is the most effective tool for detecting MS plaques, but contrast-enhanced imaging involves potential risks, including toxicity and increased imaging time. Previous methods for differentiating plaque types, such as texture analysis and manual feature extraction, face challenges such as limited datasets and poor generalizability. This study aims to develop and compare deep learning-based methods, specifically convolutional neural networks (CNNs), to classify MS lesion types using non-contrast MRI, aiming to improve clinical applicability and reduce reliance on contrast agents. This study involved 106 multiple sclerosis (MS) patients from two MRI centers. A total of 3410 lesions were analyzed, including 1408 active and 2002 inactive lesions. MRI images, including T1-weighted imaging with gadolinium contrast (T1 + Gd(, T1, Fluid-Attenuated Inversion Recovery (FLAIR), and T2 sequences, were acquired. The segmented lesions were converted into 2D slices and resampled to 128 × 128 pixels for deep learning input. Data augmentation and normalization were applied to improve model generalizability. A custom CNN model was developed and compared with four pre-trained models (ResNet50, VGG16, DenseNet121, and EfficientNetB0) using fivefold cross-validation to evaluate model performance. Performance metrics including accuracy, sensitivity, specificity, and AUC were used. The custom CNN achieved 90.15% accuracy and 94.67% AUC in FLAIR, outperforming pre-trained models. DenseNet121 showed competitive results with 88.23% accuracy and 92.86% AUC in FLAIR. Non-contrast sequences (T1, T2, and FLAIR) combined with deep learning provided promising results, reducing reliance on contrast agents. The custom CNN model excelled in classifying MS lesions across multiple MRI sequences, offering improved diagnostic accuracy and patient safety. Custom models for specialized datasets can enhance clinical outcomes, demonstrating the potential of deep learning in MS diagnosis. These findings suggest that deep learning models can be replaced with contrast agents in routine practice. Future research may explore combining CNNs with clinical features to enhance performance and interpretability.