Ensemble Learning-based Multiple Sclerosis Detection Technique Using Magnetic Resonance Imaging

Abstract

Multiple sclerosis (MS) is a complicated neurological disorder that leads to demyelination of nerve fibers in the central nervous system, causing severe symptoms and gradual impairment. Prompt and precise diagnosis of MS is essential for prompt intervention and individualized treatment planning. This research presents a new method for detecting MS: magnetic resonance imaging (MRI) data. Utilizing current progress in deep learning and ensemble learning methodologies, we use SWIN transformer and MobileNetV3-small for extracting features from MRI images. These features are then used for classification using CatBoost, XGBoost, and random forest algorithms. The suggested framework is tested and confirmed effective using the Kaggle MS database, which consists of various MRI images. The experimental findings show a remarkable average accuracy of 99.8% and a little loss of 0.07, highlighting the effectiveness of the suggested strategy in discriminating between aberrant and normal MRI pictures that indicate MS. This study enhances the field of medical image analysis by providing a precise and effective framework for automated diagnosis of MS. This framework has the potential to enhance diagnostic efficiency and improve patient outcomes. Combining deep learning feature extraction with ensemble classifiers offers a robust and easily understandable approach for diagnosing MS and has the potential to be used in clinical settings. Future research should prioritize validating the suggested technique on more extensive datasets and incorporating it into clinical practice to enhance early identification of MS and provide individualized patient treatment.