Transforming Brain Tumor Diagnosis with IVUM-Net: An Inclusive Model for MRI-Based Detection and Classification

Tejaswi Murarry Setty, Bodagala Lakshmi Devi, K. Haripriya, S. Farhanabhanu, M. Gurudhanush, G. Chandrasekar

2025

Abstract

The accurate brain tumor diagnosis that occurs within proper time intervals ensures better patient care and treatment success. The research works to create IVUM-Net which represents an innovative AI model to improve brain cancer detection along with classification using MRI information. Advanced digital image processing methods with Convolutional Neural Networks help the proposed model conduct automated tumor detection practice. IVUM-Net leverages the capabilities of Inception V3 for feature extraction, U-Net for accurate segmentation, and Multi-Class Support Vector Machine (MCSVM) for robust classification. Data augmentation together with transfer learning methods will optimize performance levels and preprocessing methods will optimize picture quality for the model. The method aims to eliminate human mistakes in addition to reducing the need for visual assessment. Class activation mapping (CAM) serves as an interpretability tool by visualizing how the model decides between classes. The research aims at verifying IVUM-Net as an effective medical instrument for early brain tumor diagnosis and classification procedures to enhance treatment approaches.

Paper Citation

in Harvard Style

Setty T., Devi B., Haripriya K., Farhanabhanu S., Gurudhanush M. and Chandrasekar G. (2025). Transforming Brain Tumor Diagnosis with IVUM-Net: An Inclusive Model for MRI-Based Detection and Classification. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 683-689. DOI: 10.5220/0013919000004919

in Bibtex Style

@conference{icrdicct`2525,
author={Tejaswi Setty and Bodagala Devi and K. Haripriya and S. Farhanabhanu and M. Gurudhanush and G. Chandrasekar},
title={Transforming Brain Tumor Diagnosis with IVUM-Net: An Inclusive Model for MRI-Based Detection and Classification},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={683-689},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013919000004919},
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 - ICRDICCT`25
TI - Transforming Brain Tumor Diagnosis with IVUM-Net: An Inclusive Model for MRI-Based Detection and Classification
SN - 978-989-758-777-1
AU - Setty T.
AU - Devi B.
AU - Haripriya K.
AU - Farhanabhanu S.
AU - Gurudhanush M.
AU - Chandrasekar G.
PY - 2025
SP - 683
EP - 689
DO - 10.5220/0013919000004919
PB - SciTePress