Author, as appears in the article.: Saffari, Nasibeh; Rashwan, Hatem A; Abdel-Nasser, Mohamed; Kumar Singh, Vivek; Arenas, Meritxell; Mangina, Eleni; Herrera, Blas; Puig, Domenec
Department: Ciències Mèdiques Bàsiques Enginyeria Informàtica i Matemàtiques Enginyeria Electrònica, Elèctrica i Automàtica
e-ISSN: 2075-4418
URV's Author/s: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / Abdelnasser Mohamed Mahmoud, Mohamed / Arenas Prat, Meritxell / Herrera Gómez, Blas / Puig Valls, Domènec Savi / Saffari Tabalvandani, Nasibeh
Keywords: Women Tissue Risk Patterns Mammograms Images Generative adversarial networks Deep learning Convolutional neural network Cancer Breast density Breast cancer
Abstract: Breast density estimation with visual evaluation is still challenging due to low contrast and significant fluctuations in the mammograms' fatty tissue background. The primary key to breast density classification is to detect the dense tissues in the mammographic images correctly. Many methods have been proposed for breast density estimation; nevertheless, most of them are not fully automated. Besides, they have been badly affected by low signal-to-noise ratio and variability of density in appearance and texture. This study intends to develop a fully automated and digitalized breast tissue segmentation and classification using advanced deep learning techniques. The conditional Generative Adversarial Networks (cGAN) network is applied to segment the dense tissues in mammograms. To have a complete system for breast density classification, we propose a Convolutional Neural Network (CNN) to classify mammograms based on the standardization of Breast Imaging-Reporting and Data System (BI-RADS). The classification network is fed by the segmented masks of dense tissues generated by the cGAN network. For screening mammography, 410 images of 115 patients from the INbreast dataset were used. The proposed framework can segment the dense regions with an accuracy, Dice coefficient, Jaccard index of 98%, 88%, and 78%, respectively. Furthermore, we obtained precision, sensitivity, and specificity of 97.85%, 97.85%, and 99.28%, respectively, for breast density classification. This study's findings are promising and show that the proposed deep learning-based techniques can produce a clinically useful computer-aided tool for breast density analysis by digital mammography.
Thematic Areas: Medicine, general & internal Internal medicine Clinical biochemistry
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: mohamed.abdelnasser@urv.cat hatem.abdellatif@urv.cat nasibeh.saffarit@estudiants.urv.cat meritxell.arenas@urv.cat domenec.puig@urv.cat blas.herrera@urv.cat
Author identifier: 0000-0002-1074-2441 0000-0001-5421-1637 0000-0003-0815-2570 0000-0002-0562-4205 0000-0003-2924-9195
Record's date: 2024-09-21
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://www.mdpi.com/2075-4418/10/11/988
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Diagnostics. 10 (11): 988-
APA: Saffari, Nasibeh; Rashwan, Hatem A; Abdel-Nasser, Mohamed; Kumar Singh, Vivek; Arenas, Meritxell; Mangina, Eleni; Herrera, Blas; Puig, Domenec (2020). Fully Automated Breast Density Segmentation and Classification Using Deep Learning. Diagnostics, 10(11), 988-. DOI: 10.3390/diagnostics10110988
Article's DOI: 10.3390/diagnostics10110988
Entity: Universitat Rovira i Virgili
Journal publication year: 2020
Publication Type: Journal Publications