Deep Learning for Accurate Detection of Multiple Sclerosis in MRI Scans

Abstract

Multiple sclerosis (MS) is a long-term disease of the nervous system that causes the central nerve system to break down. This can cause serious dysfunction. An early and correct evaluation is very important for treating and managing the illness well. MRI, or magnetic resonance imaging, is a common, non-invasive screening method used to diagnose and keep an eye on people with MS. However, doctors have to look at MRI scans by hand, which takes time, can vary from observer to observer, and is prone to human mistake. Deep learning (DL) methods, especially convolutional neural networks (CNNs), have shown a lot of promise in medical picture analysis lately. This is because they can automatically learn and pull out important features from large datasets. There is a new DL-based method in this work for correctly finding and classifying MS tumors in MRI scans. We used a fully automated CNN model that was trained on a big set of labelled MRI images. The set includes T1-weighted, T2-weighted, and FLAIR images, among others. The suggested model includes several preparation steps, such as normalization, data enhancement, and segmentation, to make the network more reliable and usable in a wider range of situations. We got our model to perform at the highest level, beating out other machine learning methods and finding MS spots with high accuracy, sensitivity, and specificity. The results show that deep learning could help make multiple sclerosis diagnosis more accurate and faster in clinical settings. Adding DL algorithms to the screening process can help doctors make better choices more quickly and correctly, which will eventually improve the health of their patients. In the future, the model will be improved and its performance will be tested on a variety of real-world clinical samples.