Interpretable and Real-Time Deep Learning Framework for Multimodal Brain Tumor Classification Using Enhanced Pre-Processed MRI Data

Srikanth Cherukuvada, Satri Tabita, Partheepan R., D. B. K. Kamesh, Priyadharshini R., S. Mohan

2025

Abstract

Accurate classification of brain tumors plays a crucial role in timely diagnosis and effective treatment planning. This research presents an advanced deep learning framework that leverages multimodal MRI data (T1, T2, FLAIR, and T1c) and robust pre-processing techniques including skull stripping, intensity normalization, and bias field correction. The proposed model adopts a 3D convolutional neural network architecture combined with channel-wise attention mechanisms to fully utilize spatial and contextual features from volumetric MRI scans. Additionally, explainable AI methods such as Grad-CAM are integrated to enhance interpretability and support clinical decision-making. The model is optimized for real-time inference through quantization and deployment on lightweight edge-compatible environments, making it suitable for use in low-resource clinical settings. Extensive experiments using cross-validation and multiple evaluation metrics accuracy, precision, recall, AUC, and Dice coefficient demonstrate the framework's superior performance in classifying tumor subtypes including glioma, meningioma, and pituitary tumors. The system achieves high diagnostic precision while maintaining low latency and transparency, bridging the gap between research and clinical utility.

Paper Citation

in Harvard Style

Cherukuvada S., Tabita S., R. P., Kamesh D., R. P. and Mohan S. (2025). Interpretable and Real-Time Deep Learning Framework for Multimodal Brain Tumor Classification Using Enhanced Pre-Processed MRI Data. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 508-514. DOI: 10.5220/0013868300004919

in Bibtex Style

@conference{icrdicct`2525,
author={Srikanth Cherukuvada and Satri Tabita and Partheepan R. and D. Kamesh and Priyadharshini R. and S. Mohan},
title={Interpretable and Real-Time Deep Learning Framework for Multimodal Brain Tumor Classification Using Enhanced Pre-Processed MRI Data},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25},
year={2025},
pages={508-514},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013868300004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25
TI - Interpretable and Real-Time Deep Learning Framework for Multimodal Brain Tumor Classification Using Enhanced Pre-Processed MRI Data
SN - 978-989-758-777-1
AU - Cherukuvada S.
AU - Tabita S.
AU - R. P.
AU - Kamesh D.
AU - R. P.
AU - Mohan S.
PY - 2025
SP - 508
EP - 514
DO - 10.5220/0013868300004919
PB - SciTePress