A multi-modal deep learning network for the classification of paramagnetic rim and remyelinated lesions in multiple sclerosis.

Key Points

• The aim is to develop a deep learning network to classify different lesion types in multiple sclerosis, enhancing diagnostic accuracy.
• Implemented a multi-modal deep learning network
• Classified paramagnetic rim lesions and remyelinated lesions
• Utilized magnetic resonance imaging for lesion detection
• Achieved high accuracy in classifying paramagnetic rim lesions
• Successfully detected remyelinated lesions
• Potential to improve personalized treatment strategies for individuals with multiple sclerosis