Optimization and Learning Rate Influence on Breast Cancer Image Classification

Gleidson Barbosa, Larissa Moreira, Larissa Moreira, Pedro Moises de Sousa, Rodrigo Moreira, André Backes

2024

Abstract

Breast cancer is a prevalent and challenging pathology, with significant mortality rates, affecting both women and men. Despite advancements in technology, such as Computer-Aided Diagnosis (CAD) and awareness campaigns, timely and accurate diagnosis remains a crucial issue. This study investigates the performance of Convolutional Neural Networks (CNNs) in predicting and supporting breast cancer diagnosis, considering BreakHis and Biglycan datasets. Through a factorial partial method, we measured the impact of optimization and learning rate factors on the prediction model accuracy. By measuring each factor’s level of influence on the validation accuracy response variable, this paper brings valuable insights into the relevance analyses and CNN behavior. Furthermore, the study sheds light on the explainability of Artificial Intelligence (AI) through factorial partial performance evaluation design. Among the results, we determine which and how much the hyperparameters tunning influenced the performance of the models. The findings contribute to image-based medical diagnosis field, fostering the integration of computational and machine learning approaches to enhance breast cancer diagnosis and treatment.

Paper Citation

in Harvard Style

Barbosa G., Moreira L., Moises de Sousa P., Moreira R. and Backes A. (2024). Optimization and Learning Rate Influence on Breast Cancer Image Classification. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 792-799. DOI: 10.5220/0012507100003660

in Bibtex Style

@conference{visapp24,
author={Gleidson Barbosa and Larissa Moreira and Pedro Moises de Sousa and Rodrigo Moreira and André Backes},
title={Optimization and Learning Rate Influence on Breast Cancer Image Classification},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={792-799},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012507100003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Optimization and Learning Rate Influence on Breast Cancer Image Classification
SN - 978-989-758-679-8
AU - Barbosa G.
AU - Moreira L.
AU - Moises de Sousa P.
AU - Moreira R.
AU - Backes A.
PY - 2024
SP - 792
EP - 799
DO - 10.5220/0012507100003660
PB - SciTePress