A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

Overview

Journal Biomed Res Int

Publisher Wiley

Specialties Biomedical Engineering
Biotechnology
General Medicine

Date 2020 May 29

PMID 32462017

Citations 13

Authors

Said Boumaraf

Xiabi Liu

Chokri Ferkous

Xiaohong Ma

Affiliations

Soon will be listed here.

Abstract

Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient's age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.

Citing Articles

MammoViT: A Custom Vision Transformer Architecture for Accurate BIRADS Classification in Mammogram Analysis.

Al Mansour A, Alshomrani F, Alfahaid A, Almutairi A Diagnostics (Basel). 2025; 15(3).

PMID: 39941215 PMC: 11817779. DOI: 10.3390/diagnostics15030285.

A Short Breast Imaging Reporting and Data System-Based Description for Classification of Breast Mass Grade.

Grande-Barreto J, Lopez-Armas G, Sanchez-Tiro J, Peregrina-Barreto H Life (Basel). 2025; 14(12.

PMID: 39768342 PMC: 11677739. DOI: 10.3390/life14121634.

TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines.

Hussain S, Naseem U, Ali M, Avendano Avalos D, Cardona-Huerta S, Bosques Palomo B BMC Med Inform Decis Mak. 2024; 24(1):310.

PMID: 39444035 PMC: 11515610. DOI: 10.1186/s12911-024-02717-7.

Lesion detection in women breast's dynamic contrast-enhanced magnetic resonance imaging using deep learning.

Saikia S, Si T, Deb D, Bora K, Mallik S, Maulik U Sci Rep. 2023; 13(1):22555.

PMID: 38110462 PMC: 10728155. DOI: 10.1038/s41598-023-48553-z.

Enhanced breast mass mammography classification approach based on pre-processing and hybridization of transfer learning models.

Boudouh S, Bouakkaz M J Cancer Res Clin Oncol. 2023; 149(16):14549-14564.

PMID: 37567987 DOI: 10.1007/s00432-023-05249-1.

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