Brain Tumor Detection Using Advanced Hybrid Approach of Deep Learning and Machine Learning

Deepa B., Geetha S., Sreesanth S., Sridhar B., Yuvarani M.

2025

Abstract

Tumors of the brain constitute one of the critical medical conditions that would require accurate and early diagnosis for effective treatment. It presented a hybrid intelligent approach that integrates the potentials of deep learning with those of other technologies for machine learning in order to solve the problem of brain tumor detection. CNNs have mined high-level of spatial features from the imaging data, capitalizing on their great feature extraction abilities. Adopting this, the features are classified using SVM and KNN. The proposed technique utilizes feature extraction in deep learning before feeding it to the standard machine learning classifiers to provide a computationally efficient and accurate diagnostic tool. Experimental results have shown that the hybrid CNN-SVM-KNN models achieved high classification performance and will, therefore, significantly help radiologists in brain tumor diagnosis. The present study enumerates the strengths of deep learning techniques in boosting the accuracy of medical image analysis and decision support systems.

Paper Citation

in Harvard Style

B. D., S. G., S. S., B. S. and M. Y. (2025). Brain Tumor Detection Using Advanced Hybrid Approach of Deep Learning and Machine Learning. 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 425-430. DOI: 10.5220/0013899500004919

in Bibtex Style

@conference{icrdicct`2525,
author={Deepa B. and Geetha S. and Sreesanth S. and Sridhar B. and Yuvarani M.},
title={Brain Tumor Detection Using Advanced Hybrid Approach of Deep Learning and Machine Learning},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={425-430},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013899500004919},
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 - Brain Tumor Detection Using Advanced Hybrid Approach of Deep Learning and Machine Learning
SN - 978-989-758-777-1
AU - B. D.
AU - S. G.
AU - S. S.
AU - B. S.
AU - M. Y.
PY - 2025
SP - 425
EP - 430
DO - 10.5220/0013899500004919
PB - SciTePress