Comparative Analysis of Brain Tumor Classification and Models Based on VGG16

Yilin Chen

2024

Abstract

This study delves into the effectiveness of the Visual Geometry Group Network 16 (VGG16) convolutional neural network (CNN) in the crucial task of classifying brain tumors, a pivotal endeavor aimed at enhancing diagnostic accuracy and tailoring patient treatment in the field of oncology. Leveraging the renowned VGG16 model, celebrated for its deep architecture and robust feature extraction capabilities, this research seeks to propel the accuracy of brain tumor diagnostics to new heights. Through a meticulously crafted methodology encompassing comprehensive image preprocessing, meticulous optimization of the VGG16 model, and meticulous comparison with other CNN models, the study meticulously evaluates crucial metrics such as accuracy, sensitivity, and specificity. Drawing upon a rich dataset of brain tumor images for analysis, the findings underscore VGG16's superior classification performance, highlighting its profound potential to revolutionize medical imaging practices and elevate the standard of patient care in oncology. These compelling results not only bolster the utilization of deep learning techniques in medical diagnostics but also pave the way for future advancements in personalized healthcare methodologies.

Paper Citation

in Harvard Style

Chen Y. (2024). Comparative Analysis of Brain Tumor Classification and Models Based on VGG16. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 652-658. DOI: 10.5220/0012961300004508

in Bibtex Style

@conference{emiti24,
author={Yilin Chen},
title={Comparative Analysis of Brain Tumor Classification and Models Based on VGG16},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={652-658},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012961300004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Comparative Analysis of Brain Tumor Classification and Models Based on VGG16
SN - 978-989-758-713-9
AU - Chen Y.
PY - 2024
SP - 652
EP - 658
DO - 10.5220/0012961300004508
PB - SciTePress