Significance of Training Images and Feature Extraction in Lesion Classification

Adél Bajcsi, Anca Andreica, Camelia Chira

2024

Abstract

Proper treatment of breast cancer is essential to increase survival rates. Mammography is a widely used, noninvasive screening method for breast cancer. A challenging task in mammogram analysis is to distinguish between tumors. In the current study, we address this problem using different feature extraction and classification methods. In the literature, numerous feature extraction methods have been presented for breast lesion classification, such as textural features, shape features, and wavelet features. In the current paper, we propose the use of shape features. In general, benign lesions have a more regular shape than malignant lesions. However, there are exceptions and in our experiments, we highlight the importance of a balanced split of these samples. Decision Tree and Random Forest methods are used for classification due to their simplicity and interpretability. A comparative analysis is conducted to evaluate the effectiveness of the classification methods. The best results were achieved using the Random Forest classifier with 96.12% accuracy using images from the Digital Dataset for Screening Mammography – DDSM.

Paper Citation

in Harvard Style

Bajcsi A., Andreica A. and Chira C. (2024). Significance of Training Images and Feature Extraction in Lesion Classification. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 117-124. DOI: 10.5220/0012308900003636

in Bibtex Style

@conference{icaart24,
author={Adél Bajcsi and Anca Andreica and Camelia Chira},
title={Significance of Training Images and Feature Extraction in Lesion Classification},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={117-124},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012308900003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Significance of Training Images and Feature Extraction in Lesion Classification
SN - 978-989-758-680-4
AU - Bajcsi A.
AU - Andreica A.
AU - Chira C.
PY - 2024
SP - 117
EP - 124
DO - 10.5220/0012308900003636
PB - SciTePress