Layer-Wise Relevance Propagation for Classifying Brain MRI Images

Ganesh Naik, Shivyogi Bendegerimath, Vijeth Kawari, Gautam Narajji, Prashant Narayankar

2025

Abstract

Accurate diagnosis and explainable predictions are important in effective planning and monitoring treatment in brain tumor analysis using medical imaging. To enhance the capabilities of tumor detection and interpretation in Brain MRI scans, the proposed work presents a comprehensive framework that combines Explainable AI (XAI) with brain tumor classification. The framework, based on ResNet18, a deep learning model, classifies MRI images into four categories: glioma tumor, meningioma tumor, pituitary tumor, and no tumor. The system incorporates Layer-wise Relevance Propagation (LRP) to highlight regions influencing predictions, providing richer interpretability and visual explanations of the decision-making process. The proposed work has demonstration of 3 approaches for explainable decision making process 1) LRP with heatmaps 2) LRP using overlayed heatmaps 3) Pixel-wise Relevance of presence of tumor. Additionally, the proposed approach includes Automated Medical Report Generation, summarizing categorization results and presenting visual explanations to assist physicians effectively. The proposed model has reached 85% accuracy with strong prediction capabilities and superior explainability in performance to adequately fulfill the fundamental demand of AI-based health solutions to provide more transparent and reliable performance.

Paper Citation

in Harvard Style

Naik G., Bendegerimath S., Kawari V., Narajji G. and Narayankar P. (2025). Layer-Wise Relevance Propagation for Classifying Brain MRI Images. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 5-11. DOI: 10.5220/0013607800004664

in Bibtex Style

@conference{incoft25,
author={Ganesh Naik and Shivyogi Bendegerimath and Vijeth Kawari and Gautam Narajji and Prashant Narayankar},
title={Layer-Wise Relevance Propagation for Classifying Brain MRI Images},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={5-11},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013607800004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Layer-Wise Relevance Propagation for Classifying Brain MRI Images
SN - 978-989-758-763-4
AU - Naik G.
AU - Bendegerimath S.
AU - Kawari V.
AU - Narajji G.
AU - Narayankar P.
PY - 2025
SP - 5
EP - 11
DO - 10.5220/0013607800004664
PB - SciTePress