Deep learning for multiple sclerosis lesion classification and stratification using MRI

Multiple sclerosis (MS) is a chronic neurological disease characterized by inflammation, demyelination, and neurodegeneration within the central nervous system. Conventional magnetic resonance imaging (MRI) techniques often struggle to detect small or subtle lesions, particularly in challenging regions such as the cortical gray matter and brainstem. This study introduces a novel deep learning-based approach, combined with a robust preprocessing pipeline and optimized MRI protocols, to improve the precision of MS lesion classification and stratification.

We designed a convolutional neural network (CNN) architecture specifically tailored for high-resolution T2-weighted imaging (T2WI), augmented by deep learning-based reconstruction (DLR) techniques. The model incorporates dual attention mechanisms, including spatial and channel attention modules, to enhance feature extraction. A comprehensive preprocessing pipeline was employed, featuring bias field correction, skull stripping, image registration, and intensity normalization. The proposed framework was trained and validated on four publicly available datasets and evaluated using precision, sensitivity, specificity, and area under the curve (AUC) metrics.

The model demonstrated exceptional performance, achieving a precision of 96.27 %, sensitivity of 95.54 %, specificity of 94.70 %, and an AUC of 0.975. It outperformed existing state-of-the-art methods, particularly in detecting lesions in underdiagnosed regions such as the cortical gray matter and brainstem. The integration of advanced attention mechanisms enabled the model to focus on critical MRI features, leading to significant improvements in lesion classification and stratification.

This study presents a novel and scalable approach for MS lesion detection and classification, offering a practical solution for clinical applications. By integrating advanced deep learning techniques with optimized MRI protocols, the proposed framework achieves superior diagnostic accuracy and generalizability, paving the way for enhanced patient care and more personalized treatment strategies. This work sets a new benchmark for MS diagnosis and management in both research and clinical practice.