Scalable and Robust CNN Models for Brain Tumor Detection in Healthcare Applications

Reshma Shinde, Vijay A. Sangolagi, Mithun B. Patil, Vikas Mhetre, Sarvesh Kulkarni

2025

Abstract

Successful detection of brain tumors plays a vital role in patients obtaining an early diagnosis and developing proper treatment strategies which enhance survival rates. The clinical diagnosis process driven by MRI produces slow results with human inaccuracies which calls for automated techniques. The researchers present a deep learning platform that combines CNNs with EfficientNet-B0 for better brain tumor detection at a computational speed that remains high. MRI scan spatial features are extracted by CNNs together with EfficientNet-B0 performs compound adjustments to maximize its depth width and resolution parameters for superior operations. The dataset consists of a wide range of MRI scans that are manually labeled for brain tumors with multiple data augmentation methods used to enhance model universal operation. Research findings show the proposed system accomplishes better accuracy rates and precision along with recall metrics and F1-score than standard deep learning techniques. The addition of advanced regularization methods combined with contrast enhancement helps lower overfitting risk for reliable prediction outcomes. The model design maintains high performance at clinical diagnosis speeds which makes it functional for real-time practice in hospitals. The advantages of EfficientNet-B0 emerge from its performance against other available CNN architectures in medical imaging applications. The Research will concentrate on modifying the model to excel at multi-class tumor classification while adding explainable AI for better understanding and proving its clinical impact in true medical environments.

Paper Citation

in Harvard Style

Shinde R., Sangolagi V., Patil M., Mhetre V. and Kulkarni S. (2025). Scalable and Robust CNN Models for Brain Tumor Detection in Healthcare Applications. 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 346-353. DOI: 10.5220/0013912800004919

in Bibtex Style

@conference{icrdicct`2525,
author={Reshma Shinde and Vijay Sangolagi and Mithun Patil and Vikas Mhetre and Sarvesh Kulkarni},
title={Scalable and Robust CNN Models for Brain Tumor Detection in Healthcare Applications},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={346-353},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013912800004919},
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 - Scalable and Robust CNN Models for Brain Tumor Detection in Healthcare Applications
SN - 978-989-758-777-1
AU - Shinde R.
AU - Sangolagi V.
AU - Patil M.
AU - Mhetre V.
AU - Kulkarni S.
PY - 2025
SP - 346
EP - 353
DO - 10.5220/0013912800004919
PB - SciTePress