Fully Automated Breast Density Segmentation and Classification Using Deep Learning
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 b...
Main Authors: | Nasibeh Saffari, Hatem A. Rashwan, Mohamed Abdel-Nasser, Vivek Kumar Singh, Meritxell Arenas, Eleni Mangina, Blas Herrera, Domenec Puig |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-11-01
|
Series: | Diagnostics |
Subjects: | |
Online Access: | https://www.mdpi.com/2075-4418/10/11/988 |
Similar Items
-
Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images
by: Dilovan Asaad Zebari, et al.
Published: (2020-01-01) -
Suspicious and malignant features on mammogram among women in a group of communities within south east Nigeria
by: Eric O Umeh, et al.
Published: (2019-01-01) -
Segmentation-Based Fractal Texture Analysis (SFTA) to Detect Mass in Mammogram Images
by: IRMA AMELIA DEWI, et al.
Published: (2021-01-01) -
Impact of same day screening mammogram results on women’s satisfaction and overall breast cancer screening experience: a quality improvement survey analysis
by: Biren A. Shah, et al.
Published: (2022-08-01) -
Mammograms in cosmetic breast surgery
by: Shiffman M
Published: (2005-01-01)