Identification of Multiple Sclerosis Using Artificial Neural Networks

Abstract

Multiple sclerosis (MS) is a chronic neurological disorder affecting the central nervous system, causing a range of symptoms including vision problems, fatigue, and motor dysfunction. Early and accurate diagnosis is crucial for effective treatment and management of the disease. Magnetic Resonance Imaging (MRI) plays a vital role in identifying MS, but manual interpretation of MRI scans is time-consuming and prone to human error. In this study, we aim to enhance the diagnostic process by employing artificial neural networks (ANNs), specifically Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) networks, to identify MS from MRI data. The research involves data preprocessing, feature extraction, and model training using neural network architectures. The performance of these models is evaluated based on accuracy, sensitivity, and specificity. The results show that the LSTM model outperforms other methods in terms of diagnostic accuracy, offering a promising tool for MS detection in clinical settings. This work contributes to the integration of machine learning techniques in medical diagnostics and emphasizes the potential of neural networks for improving healthcare outcomes.