Hybrid CNN-ResNet50 Model for Brain Tumor Classification Using Transfer Learning

Sheenam Middha, Sonam Khattar, Tushar Verma

2025

Abstract

A tumor is fatal cancers that can affect both adults and minors. A brain tumor's treatment depends on an early and precise diagnosis. Finding the brain tumor with computer-aided technologies is a crucial first step for physicians. Experts can spot tumors more quickly and easily thanks to these devices. But conventional procedures also prevent mistakes from happening. This article uses magnetic resonance imaging (MRI) to diagnose brain tumors. A hybrid approach that uses CNN models-one of the deep learning networks-for diagnosis has been put forth. One of the CNN models, Resnet50 architecture, serves as the foundation.97.67% accuracy rate is achieved with this model. The model that performed the best out of all of them has been used to classify the images of brain tumors. Consequently, further analyses in the literature indicate that the suggested method is practical and useful for brain tumor detection in computer-aided systems.

Paper Citation

in Harvard Style

Middha S., Khattar S. and Verma T. (2025). Hybrid CNN-ResNet50 Model for Brain Tumor Classification Using Transfer Learning. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 444-448. DOI: 10.5220/0013594400004664

in Bibtex Style

@conference{incoft25,
author={Sheenam Middha and Sonam Khattar and Tushar Verma},
title={Hybrid CNN-ResNet50 Model for Brain Tumor Classification Using Transfer Learning},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={444-448},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013594400004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Hybrid CNN-ResNet50 Model for Brain Tumor Classification Using Transfer Learning
SN - 978-989-758-763-4
AU - Middha S.
AU - Khattar S.
AU - Verma T.
PY - 2025
SP - 444
EP - 448
DO - 10.5220/0013594400004664
PB - SciTePress