Novel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms-Reference-Cited by-同舟云学术

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Novel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms

Published:2008-01 Issue:1 Volume:42 Page:67-79
ISSN:0933-3657
Container-title:Artificial Intelligence in Medicine
language:en
Short-container-title:Artificial Intelligence in Medicine

Author:

Verma Brijesh

Publisher

Elsevier BV

Subject

Artificial Intelligence,Medicine (miscellaneous)

Reference44 articles.

1. Parkin M, Freddie Bray F, Ferlay J, Pisani P. Global Cancer Statistics, A Cancer Journal for Clinicians, vol. 55, American Cancer Society; 2005. p. 74–108.

2. Breast Cancer Facts; 2005. www.breastcancerfund.org (accessed September 12, 2007).

3. National Breast Cancer Centre, http://www.nbcc.org.au/bestpractice/statistics/ (accessed September 12, 2007).

4. American College of Radiology. www.acr.org (accessed September 12, 2007).

5. NSW Breast Cancer Institute, http://www.bci.org.au (Accessed September 12, 2007).

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2. Automated breast cancer detection by reconstruction independent component analysis (RICA) based hybrid features using machine learning paradigms;Connection Science;2022-11-28

3. Radiomic Feature Reduction Approach to Predict Breast Cancer by Contrast-Enhanced Spectral Mammography Images;Diagnostics;2021-04-10

4. Mammogram mass segmentation and detection using Legendre neural network-based optimal threshold;Medical & Biological Engineering & Computing;2021-04

5. Ensemble Discrete Wavelet Transform and Gray-Level Co-Occurrence Matrix for Microcalcification Cluster Classification in Digital Mammography;Applied Sciences;2019-12-09

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