Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques.

ABSTRACT:

Introduction

Gadolinium-based T1-weighted MRI sequence is the gold standard for the detection of active multiple sclerosis (MS) lesions. The performance of machine learning (ML) and deep learning (DL) models in the classification of active and non-active MS lesions from the T2-weighted MRI images has been investigated in this study.

Methods

107 Features of 75 active and 100 non-active MS lesions were extracted by using SegmentEditor and Radiomics modules of 3D slicer software. Sixteen ML and one sequential DL models were created using the 5-fold cross-validation method and each model with its special optimized parameters trained using the training-validation datasets. Models' performances in test data set were evaluated by metric parameters of accuracy, precision, sensitivit

SUBMITTER: Rostami A 

PROVIDER: S-EPMC11660597 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature