Computer-aided breast cancer detection using mammograms: a review
The American Cancer Society (ACS) recommends women aged 40 and above to have a
mammogram every year and calls it a gold standard for breast cancer detection. Early …
mammogram every year and calls it a gold standard for breast cancer detection. Early …
Breast cancer detection using extreme learning machine based on feature fusion with CNN deep features
A computer-aided diagnosis (CAD) system based on mammograms enables early breast
cancer detection, diagnosis, and treatment. However, the accuracy of the existing CAD …
cancer detection, diagnosis, and treatment. However, the accuracy of the existing CAD …
Preprocessing of breast cancer images to create datasets for deep-CNN
Breast cancer is the most diagnosed cancer in Australia with crude incidence rates
increasing drastically from 62.8 at ages 35-39 to 271.4 at ages 50-54 (cases per 100,000 …
increasing drastically from 62.8 at ages 35-39 to 271.4 at ages 50-54 (cases per 100,000 …
Hierarchical convolutional neural networks for segmentation of breast tumors in MRI with application to radiogenomics
Breast tumor segmentation based on dynamic contrast-enhanced magnetic resonance
imaging (DCE-MRI) is a challenging problem and an active area of research. Particular …
imaging (DCE-MRI) is a challenging problem and an active area of research. Particular …
A review on automatic mammographic density and parenchymal segmentation
Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause
(s) of breast cancer still remains unknown. Early detection, precise identification of women at …
(s) of breast cancer still remains unknown. Early detection, precise identification of women at …
Fuzzy C-means and region growing based classification of tumor from mammograms using hybrid texture feature
Identifying abnormality using breast mammography is a challenging task for radiologists due
to its nature. A more consistent and precise imaging based CAD system plays a vital role in …
to its nature. A more consistent and precise imaging based CAD system plays a vital role in …
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 …
significant fluctuations in the mammograms' fatty tissue background. The primary key to …
PCA-PNN and PCA-SVM based CAD systems for breast density classification
Early prediction of breast density is clinically significant as there is an association between
the risk of breast cancer development and breast density. In the present work, the …
the risk of breast cancer development and breast density. In the present work, the …
Fully automated breast boundary and pectoral muscle segmentation in mammograms
Breast and pectoral muscle segmentation is an essential pre-processing step for the
subsequent processes in computer aided diagnosis (CAD) systems. Estimating the breast …
subsequent processes in computer aided diagnosis (CAD) systems. Estimating the breast …
[HTML][HTML] Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks
Abstract Background and Objective The processing of medical image is an important tool to
assist in minimizing the degree of uncertainty of the specialist, while providing specialists …
assist in minimizing the degree of uncertainty of the specialist, while providing specialists …