Enhancing Multiple Sclerosis Diagnosis: Deep Transfer Learning Techniques for MRI Image Analysis (P3-1.003)

To investigate the potential of artificial intelligence (AI) and deep transfer learning techniques to enhance the detection and analysis of multiple sclerosis (MS)-specific lesions in MRI scans.

Authors

Omar Alomarı (ORCID: https://orcid.org/0000-0003-3651-4129)
Fatma Sibel Bayramoğlu
Cemil Aydın (ORCID: https://orcid.org/0000-0002-7271-5748)
Merve Akçay (ORCID: https://orcid.org/0000-0002-5878-6961)
Yılmaz Kaya (ORCID: https://orcid.org/0000-0001-5167-1101)
Baran Baytar

Institutions

Bingöl University (TR)
State Hospital (GB)
Batman University (TR)
Sağlık Bilimleri Üniversitesi (TR)
Kocaeli Üniversitesi (TR)

Publication Details

Journal: Neurology
Published: 2025-04-07
DOI: https://doi.org/10.1212/wnl.0000000000211359
Primary Topic: Brain Tumor Detection and Classification
Type: article
Field-Weighted Citation Impact: 0.00

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article

Enhancing Multiple Sclerosis Diagnosis: Deep Transfer Learning Techniques for MRI Image Analysis (P3-1.003)

Omar Alomarı, Fatma Sibel Bayramoğlu, Cemil Aydın, Merve Akçay et al.

Apr 7, 2025

Neurology

Brain Tumor Detection and Classification

article

Enhancing Multiple Sclerosis Diagnosis: Deep Transfer Learning Techniques for MRI Image Analysis (P3-1.003)

Omar Alomarı, Fatma Sibel Bayramoğlu, Cemil Aydın, Merve Akçay, Yılmaz Kaya, Baran Baytar

April 7, 2025article en

Abstract

To investigate the potential of artificial intelligence (AI) and deep transfer learning techniques to enhance the detection and analysis of multiple sclerosis (MS)-specific lesions in MRI scans.

NeurologyVol. 104(7_Supplement_1)

Bingöl University (TR), State Hospital (GB), Batman University (TR), Sağlık Bilimleri Üniversitesi (TR), Kocaeli Üniversitesi (TR)

Openalex Percentile: Top 91%

Brain Tumor Detection and Classification

DOI Source OpenAlex

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