Breast Cancer Image Classification Using Deep Learning and Test-Time Augmentation

João Mari, Larissa Moreira, Leandro Silva, Mauricio Escarpinati, André Backes

2025

Abstract

Deep learning-based computer vision methods can improve diagnostic accuracy, efficiency, and productivity. While traditional approaches primarily apply Data Augmentation (DA) during the training phase, Test-Time Augmentation (TTA) offers a complementary strategy to improve the predictive capabilities of trained models without increasing training time. In this study, we propose a simple and effective TTA strategy to enhance the classification of histopathological images of breast cancer. After optimizing hyperparameters, we evaluated the TTA strategy across all magnifications of the BreakHis dataset using three deep learning architectures, trained with and without DA. We compared five sets of transformations and multiple prediction rounds. The proposed strategy significantly improved the mean accuracy across all magnifications, demonstrating its effectiveness in improving model performance.

Paper Citation

in Harvard Style

Mari J., Moreira L., Silva L., Escarpinati M. and Backes A. (2025). Breast Cancer Image Classification Using Deep Learning and Test-Time Augmentation. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 761-768. DOI: 10.5220/0013359200003912

in Bibtex Style

@conference{visapp25,
author={João Mari and Larissa Moreira and Leandro Silva and Mauricio Escarpinati and André Backes},
title={Breast Cancer Image Classification Using Deep Learning and Test-Time Augmentation},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={761-768},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013359200003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Breast Cancer Image Classification Using Deep Learning and Test-Time Augmentation
SN - 978-989-758-728-3
AU - Mari J.
AU - Moreira L.
AU - Silva L.
AU - Escarpinati M.
AU - Backes A.
PY - 2025
SP - 761
EP - 768
DO - 10.5220/0013359200003912
PB - SciTePress