Discrimination of invasive ductal and lobular carcinoma of the breast based on the combination of enhanced Legendre polynomial, kinetic features and deep learning features

Journal article

Hasan, A. M., Al-Waely, N. K. N., Aljobouri, H. K., Jalab, H. A., Rabha W. Ibrahim, R. W. and Meziane, F. 2025. Discrimination of invasive ductal and lobular carcinoma of the breast based on the combination of enhanced Legendre polynomial, kinetic features and deep learning features. Biomedical Signal Processing and Control. 104, pp. 1-12. https://doi.org/10.1016/j.bspc.2025.107546
AuthorsHasan, A. M., Al-Waely, N. K. N., Aljobouri, H. K., Jalab, H. A., Rabha W. Ibrahim, R. W. and Meziane, F.
Abstract

The fifth most common cause of cancer-related deaths among women worldwide is breast cancer, which is the most common cancer among women globally. Early detection of breast cancer through regular screenings and awareness of symptoms can lead to better prognosis and more effective treatment options. Breast cancer is a diverse disease that comes in more than 20 varieties. It is generally divided into two categories based on his tology: in-situ carcinoma and invasive (infiltrating) carcinoma. These categories are further divided into four
subcategories based on the location of the tumor’s origin: invasive ductal carcinoma (IDC), invasive lobular
carcinoma (ILC), lobular carcinoma in situ (LCIS), and ductal carcinoma in situ (DCIS). The aim of this study is to
use medical image processing and machine learning to accurately diagnose invasive lobular carcinoma (ILC) and
invasive and lobular carcinoma (IDC) of the breast. This study develops a novel hybrid feature extraction model
to improve the diagnosis accuracy of breast cancer. Deep learning, kinetic, and enhanced Legendre polynomial
features have been combined to create a hybrid feature model that enhances IDC and ILC discrimination. The
proposed model consists of four stages: data collection, preprocessing, feature extraction, and classification. The
publicly available DCE-MRI dataset was used in this study, and the results showed classification accuracy of
97.99% in combined post-contrast-1 model. Overall, the results demonstrated the benefits of the hybrid feature
extraction model and the fact that this study is non-invasive, uses only medical image processing, and does not
require biopsies to enhance treatments.

KeywordsBreast cancer; ILC ; IDC ; ELP ; Kinetic features ; Deep learning
Year2025
JournalBiomedical Signal Processing and Control
Journal citation104, pp. 1-12
PublisherElsevier
ISSN1746-8108
1746-8094
Digital Object Identifier (DOI)https://doi.org/10.1016/j.bspc.2025.107546
Web address (URL)https://www.sciencedirect.com/science/article/pii/S1746809425000576?via%3Dihub
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Output statusPublished
Publication dates
Online27 Jan 2025
Publication process dates
Accepted19 Jan 2025
Deposited06 Mar 2025
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Regression analysis for energy and lifetime prediction in large wireless sensor networks
Mir, F, Bounceur, A and Meziane, F. 2014. Regression analysis for energy and lifetime prediction in large wireless sensor networks. 2014 International Conference on Advanced Networking Distributed Systems and Applications. Bejaia, Algeria 17 - 19 Jun 2014 IEEE. https://doi.org/10.1109/inds.2014.8
The effect of a different environment on providing personal information when engaging in E-commerce : the case of Saudis in the UK
Alshehri, H and Meziane, F. 2014. The effect of a different environment on providing personal information when engaging in E-commerce : the case of Saudis in the UK. 22nd IBIMA Conference. Rome, Italy 13 - 14 Nov 2013 IBIMA.
The Effect of recommendations from family members and friends and third party endorsement in B2C E-Commerce: The case of Saudi Arabia
Alshehri, H. and Meziane, F. 2014. The Effect of recommendations from family members and friends and third party endorsement in B2C E-Commerce: The case of Saudi Arabia. 24th International Business Information Management Association Conference - Crafting Global Competitive Economies: 2020 Vision Strategic Planning and Smart Implementation. Milan, Italy 06 - 07 Nov 2014 IBIMA.