Author ORCID Identifier

https://orcid.org/0000-0002-3586-7363

Document Type

Conference Paper

Disciplines

1.2 COMPUTER AND INFORMATION SCIENCE, Computer Sciences, Information Science, Bioinformatics

Publication Details

https://digital-library.theiet.org/doi/abs/10.1049/icp.2024.3308

https://doi.org/10.1049/icp.2024.3308

Abstract

Multiple sclerosis (MS) is a complex neurological disorder that requires precise diagnosis for effective treatment. This study aims to enhance MS diagnosis by integrating eXplainable Artificial Intelligence (XAI) techniques into a convolutional neural network (CNN) framework. The proposed model achieves high accuracy and provides visual explanations of its predictions. Using the Gradient-weighted Class Activation Mapping (Grad-CAM) method, it highlights the most important regions in MRI images influencing the model’s decisions, adding transparency and trust to the diagnostic process. The CNN, trained on a dataset of FLAIR MRI images, demonstrates superior performance compared to existing models, with a final accuracy of 99.36%. This work contributes to the growing field of XAI in healthcare, offering a robust and interpretable tool for MS diagnosis.

DOI

https://doi.org/10.1049/icp.2024.3308

Funder

Science Foundation Ireland Center for Research Training in Machine Learning

Creative Commons License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

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