Explaining Mammographic Texture: The Role of View and Abnormality Type in Early Cancer Diagnosis

Bianca Iacob, Laura Diosan

2025

Abstract

Detecting breast cancer at an early stage significantly increases the chances of successful treatment and survival. Understanding the full topology of various abnormalities requires analyzing multiple mammography views. This study evaluates the performance of mammographic views in detecting abnormalities, focusing on calcifications and masses, to enhance early cancer diagnosis. By examining the importance of considering both the type of abnormality and the mammographic view, we aim to identify key factors influencing detection accuracy. Additionally, we investigate whether incorporating textural features such as GLCM, GLRLM, and GLSZM can improve overall model performance. Our findings underscore the necessity of a tailored approach in mammographic analysis. These insights are crucial for advancing early diagnostic capabilities and improving patient outcomes.

Paper Citation

in Harvard Style

Iacob B. and Diosan L. (2025). Explaining Mammographic Texture: The Role of View and Abnormality Type in Early Cancer Diagnosis. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 124-131. DOI: 10.5220/0013096900003890

in Bibtex Style

@conference{icaart25,
author={Bianca Iacob and Laura Diosan},
title={Explaining Mammographic Texture: The Role of View and Abnormality Type in Early Cancer Diagnosis},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={124-131},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013096900003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Explaining Mammographic Texture: The Role of View and Abnormality Type in Early Cancer Diagnosis
SN - 978-989-758-737-5
AU - Iacob B.
AU - Diosan L.
PY - 2025
SP - 124
EP - 131
DO - 10.5220/0013096900003890
PB - SciTePress