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

The performance of sixteen ML and one sequential DL models in the classification of active and non-active MS lesions was evaluated. The results of the study highlight the effectiveness of sequential DL approach and ensemble methods in achieving robust predictive performance, underscoring their potential applications in classifying MS plaques.

Authors

Atefeh Rostami (ORCID: https://orcid.org/0000-0002-4736-4181)
Amir Raoofi (ORCID: https://orcid.org/0000-0001-7402-1726)
Amir Dareyni
Mostafa Robatjazi (ORCID: https://orcid.org/0000-0003-4522-1360)
Ali Ghorbani (ORCID: https://orcid.org/0000-0002-4817-7476)
Omid Ganji
Mahdiye Siyami

Institutions

Iran University of Medical Sciences (IR)
University of Tehran (IR)
Sina Hospital (IR)
Sabzevar University of Medical Sciences (IR)
Tehran University of Medical Sciences (IR)

Publication Details

Journal: BMC Medical Imaging
Published: 2024-12-20
DOI: https://doi.org/10.1186/s12880-024-01528-6
Citations: 8
Primary Topic: Radiomics and Machine Learning in Medical Imaging
Type: article
Field-Weighted Citation Impact: 3.23

Funders

Sabzevar University of Medical Sciences

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article

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

Atefeh Rostami, Amir Raoofi, Amir Dareyni, Mostafa Robatjazi et al.

Dec 20, 2024

8 citations

BMC Medical Imaging

Radiomics and Machine Learning in Medical Imaging

3.23

article

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

Atefeh Rostami, Amir Raoofi, Amir Dareyni, Mostafa Robatjazi, Ali Ghorbani, Omid Ganji, Mahdiye Siyami

December 20, 2024article en

8 citations

Abstract

The performance of sixteen ML and one sequential DL models in the classification of active and non-active MS lesions was evaluated. The results of the study highlight the effectiveness of sequential DL approach and ensemble methods in achieving robust predictive performance, underscoring their potential applications in classifying MS plaques.

BMC Medical ImagingVol. 24(1)

Iran University of Medical Sciences (IR), University of Tehran (IR), Sina Hospital (IR), Sabzevar University of Medical Sciences (IR), Tehran University of Medical Sciences (IR)

Sabzevar University of Medical Sciences

Openalex Percentile: Top 7%

Radiomics and Machine Learning in Medical Imaging

3.23

DOI Open Access PDF OpenAlex

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Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques — Atefeh Rostami, Amir Raoofi, et al. · BMC Medical Imaging (2024) | TGRS Research Map | TGRS