MRI-Based Brain Lesion Detection for Multiple Sclerosis Using Deep Learning

Abstract

Multiple Sclerosis (MS) is a long-term autoimmune condition that affects the nervous system, causing inflammation, damage to the myelin sheath, and gradual loss of nerve function. One key sign of MS is the presence of lesions in the brain and spinal cord, which are often found using a scan called Magnetic Resonance Imaging (MRI). Spotting and separating these lesions is very important for early diagnosis, keeping track of the disease, and planning treatment. But doing this by hand takes a lot of time, and different doctors may see things differently, especially when the lesions are small, hard to see, or spread out.

To fix these problems, this study suggests an automatic system for finding and separating MS lesions using deep learning techniques. The system uses different types of MRI images, especially FLAIR and T1-weighted scans, since they show lesions more clearly. It uses a type of neural network called 3D U-Net to accurately separate each lesion at the smallest level. To prepare the data properly, the system includes steps like removing unnecessary parts of the brain, making the image brightness consistent, and adding more images to help the system learn better.

The main goal of this research is to create a fast, reliable, and fully automatic system that can find MS lesions with very little help from a person. This system can make the work of doctors easier, improve the accuracy of diagnosis, and help keep track of the disease over time. The system's performance is checked using standard tests like the Dice Similarity Coefficient, Precision, Recall, and lesion-wise sensitivity. The results show that this approach is much better at finding and separating lesions, which helps doctors make better decisions and provide better care for patients.