Classifying region of interests from mammograms with breast cancer into BIRADS using Artificial Neural Networks-Reference-Cited by-同舟云学术

Classifying region of interests from mammograms with breast cancer into BIRADS using Artificial Neural Networks

Published:2017-05 Issue:3 Volume:2 Page:233-240
ISSN:2415-6698
Container-title:Advances in Science, Technology and Engineering Systems Journal
language:
Short-container-title:Adv. sci. technol. eng. syst. j.

Author:

Avalos-Rivera Estefanía D.,Pastrana-Palma Alberto de J.

Publisher

ASTES Journal

Subject

Management of Technology and Innovation,Physics and Astronomy (miscellaneous),Engineering (miscellaneous)

Link

http://www.astesj.com/publications/ASTESJ_020332.pdf

Reference22 articles.

1. E. D. Avalos-Rivera and A. D. J. Pastrana-Palma, "Classifying microcalcifications on digital mammography using morphological descriptors and artificial neural network," IEEE Congr. Argentino Ciencias la Informática y Desarro. Investig., 2016.

2. H. D. Cheng, X. Cai, X. Chen, L. Hu, and X. Lou, "Computer-aided detection and classification of microcalcifications in mammograms: a survey," Pattern Recognit., vol. 36, no. 12, pp. 2967-2991, Dec. 2003.

3. W. Jian, X. Sun, and S. Luo, "Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform.," Biomed. Eng. Online, vol. 11, p. 96, 2012.

4. R. Audelo-Valverde and A. Pastrana-Palma, "Clasificación de Microcalcificaciones utilizando Descriptores Morfológicos," Universidad Autonoma de Queretaro, 2014.

5. A. Tirtajaya and D. D. Santika, "Classification of Microcalcification Using Dual-Tree Complex Wavelet Transform and Support Vector Machine," in 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies, 2010, pp. 164-166.

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. BI-RADS Classification of Calcification on Mammograms;Medical Image Computing and Computer Assisted Intervention – MICCAI 2021;2021

2. Application of Feature Extraction for Breast Cancer using One Order Statistic, GLCM, GLRLM, and GLDM;Advances in Science, Technology and Engineering Systems Journal;2019