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

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...

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Bibliographic Details
Main Authors: Atefeh Rostami, Mostafa Robatjazi, Amir Dareyni, Ali Ramezan Ghorbani, Omid Ganji, Mahdiye Siyami, Amir Reza Raoofi
Format: Article
Language:English
Published: BMC 2024-12-01
Series:BMC Medical Imaging
Subjects:
Multiple Sclerosis
Active lesions
Machine Learning
Deep Learning
Feature importance
Online Access:https://doi.org/10.1186/s12880-024-01528-6
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https://doi.org/10.1186/s12880-024-01528-6

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