MS Lesion Diagnosis and Classification

Project Overview #

Deep learning solution for diagnosing and classifying multiple sclerosis (MS) lesions using FLAIR MRI images with UNet neural network architecture.

Key Features #

• Lesion Segmentation: Precise pixel-level segmentation of MS lesions in MRI images
• Classification System: Automated classification of lesions by type and severity
• FLAIR MRI Processing: Specialized in processing FLAIR (Fluid Attenuated Inversion Recovery) MRI sequences
• Automated Detection: System for automatic lesion identification and boundary detection

Technologies Used #

• PyTorch: Deep learning framework for model development
• UNet Architecture: Neural network specifically designed for medical image segmentation
• Medical Image Processing: Advanced preprocessing techniques for MRI data
• Diagnostic Assistance: Provides automated support for radiologists
• Consistency: Reduces inter-observer variability in lesion assessment
• Efficiency: Significantly reduces time required for MS lesion analysis
• Research Tool: Enables large-scale MS research studies

Technologies Used #

• Deep Learning: PyTorch, UNet architecture, medical image segmentation
• Image Processing: Medical imaging protocols, FLAIR MRI analysis
• Validation: Clinical validation frameworks, performance metrics
• Deployment: Model optimization for clinical environments