Feature Extraction through MS Lesion and Cerebral Lobe Segmentation for Enhanced EDSS Prediction
In this paper, we present a hybrid approach for segmenting multiple sclerosis (MS) lesions and predicting disability progression. Using Concat-UNet for accurate MS lesion segmentation and a multi-class UNet for brain lobe segmentation, we enable regional analysis of lesion impact. By fusing MRI, clinical, and demographic data, we enhanced EDSS prediction, with LightGBM achieving a high R2 score (0.96 ± 0.07)
Authors
- Nada Haj Messaoud (ORCID: https://orcid.org/0000-0001-6243-1373)
- Rihab Ben Dhia (ORCID: https://orcid.org/0000-0001-9244-6568)
- Asma Ben Abdallah
- Manel Mili (ORCID: https://orcid.org/0000-0003-3892-8579)
- Mohamed Hédi Bedoui (ORCID: https://orcid.org/0000-0003-4846-1722)
- Neirez Zina
Institutions
- University of Monastir (TN)
Publication Details
- Published
- 2025-01-28
- DOI
- https://doi.org/10.1109/isnib64820.2025.10983385
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.00