Diagnosis of Multiple Sclerosis Lesion using Deep Learning Models

Abstract

Exploring the identification of Multiple Sclerosis (MS) lesions involves leveraging diverse machine learning classifiers. Using Magnetic Resonance Imaging (MRI) scans, the study aims to detect and characterize these lesions, evaluating their attributes, progression stages, and the pivotal role of Artificial Intelligence (AI) in diagnosis. The focus is on analyzing automated detection algorithms, particularly Deep Learning techniques. Through comprehensive assessment, various classifiers including MLP Classifier, Random Forest (RF), Support Vector Machine (SVM), and DL are evaluated using metrics like precision, recall, F1-score, and accuracy. The DL classifier stands out, achieving remarkable precision (99%), recall (99%), and overall accuracy (99.5%). Comparative analysis confirms its superiority, reinforcing its efficacy over alternative methods. The research underscores the DL model's potential in generalizing to new samples due to its robustness and precision. This study significantly advances automated MS lesion detection, highlighting the promise of AI-based methodologies in medical image analysis.