Detection of Brain Tumors Using Advanced Image Processing and the Ensemble Model and YOLO Family

P. Poorna Priya, N. Vidhya Sree

2025

Abstract

Effective diagnosis and therapy of brain tumors depend on their identification and classification. Using the Brain Tumor dataset, this study makes use of sophisticated transfer learning models and deep convolutional neural networks (DCNNs). When models like DCNN, ResNet152, EfficientNetB2, Exception, and Nonmobile were tested, an ensemble of Exception and Nonmobile produced the best accuracy (98.1%). Grade 0 (no malignancy) to Grade III (big tumor) were the four grades into which tumors were divided. With a mean average precision (map) of 78.9%, YOLOv9 fared better than other models for anomaly detection. A Flask-based interactive interface was created for safe and easy access in order to improve usage.

Paper Citation

in Harvard Style

Priya P. and Sree N. (2025). Detection of Brain Tumors Using Advanced Image Processing and the Ensemble Model and YOLO Family. 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 628-633. DOI: 10.5220/0013939700004919

in Bibtex Style

@conference{icrdicct`2525,
author={P. Priya and N. Sree},
title={Detection of Brain Tumors Using Advanced Image Processing and the Ensemble Model and YOLO Family},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={628-633},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013939700004919},
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 - Detection of Brain Tumors Using Advanced Image Processing and the Ensemble Model and YOLO Family
SN - 978-989-758-777-1
AU - Priya P.
AU - Sree N.
PY - 2025
SP - 628
EP - 633
DO - 10.5220/0013939700004919
PB - SciTePress