Bayesian Classifier with Simplified Learning Phase for Detecting Microcalcifications in Digital Mammograms
Detection of clustered microcalcifications (MCs) in mammograms represents a significant step towards successful detection of breast cancer since their existence is one of the early signs of cancer. In this paper, a new framework that integrates Bayesian classifier and a pattern synthesizing scheme f...
Saved in:
Main Authors: | Imad Zyout, Ikhlas Abdel-Qader, Christina Jacobs |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2009-01-01
|
Series: | International Journal of Biomedical Imaging |
Online Access: | http://dx.doi.org/10.1155/2009/767805 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Breast Suspicious Microcalcifications on Contrast-Enhanced Mammograms: Practice and Reflection
by: Zhao X
Published: (2025-01-01) -
A New GLLD Operator for Mass Detection in Digital Mammograms
by: N. Gargouri, et al.
Published: (2012-01-01) -
A Computer-Aided Diagnosis System for Breast Cancer Using Independent Component Analysis and Fuzzy Classifier
by: Ikhlas Abdel-Qader, et al.
Published: (2008-01-01) -
Independent Component Analysis to Detect Clustered Microcalcification Breast Cancers
by: R. Gallardo-Caballero, et al.
Published: (2012-01-01) -
Leveraging paired mammogram views with deep learning for comprehensive breast cancer detection
by: Jae Won Seo, et al.
Published: (2025-02-01)