Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection
Pattern Recognition Letters
Artificial Intelligence in Medicine
Information Sciences: an International Journal
Mobile Agents Using Data Mining for Diagnosis Support in Ubiquitous Healthcare
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Health Monitor Agent Based on Neural Networks for Ubiquitous Healthcare Environment*
KES-AMSTA '09 Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Impact of multiple clusters on neural classification of ROIs in digital mammograms
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
PET: An expert system for productivity analysis
Expert Systems with Applications: An International Journal
Identification of tiny and large calcification in breast: a study on mammographic image analysis
International Journal of Bioinformatics Research and Applications
Effective recognition of MCCs in mammograms using an improved neural classifier
Engineering Applications of Artificial Intelligence
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
A graph-based method for detecting and classifying clusters in mammographic images
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
The refinement of microcalcification cluster assessment by joint analysis of MLO and CC views
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Microcalcification patterns recognition based combination of autoassociator and classifier
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging
Knowledge-Based Systems
Joint analysis of multiple mammographic views in CAD systems for breast cancer detection
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Diagnosing Breast Masses in Digital Mammography Using Feature Selection and Ensemble Methods
Journal of Medical Systems
A Swarm Optimized Neural Network System for Classification of Microcalcification in Mammograms
Journal of Medical Systems
A fuzzy rule-based approach for characterization of mammogram masses into BI-RADS shape categories
Computers in Biology and Medicine
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An intelligent computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing microcalcification patterns earlier and faster than typical screening programs. In this paper, we present a system based on fuzzy-neural and feature extraction techniques for detecting and diagnosing microcalcifications' patterns in digital mammograms. We have investigated and analyzed a number of feature extraction techniques and found that a combination of three features (such as entropy, standard deviation and number of pixels) is the best combination to distinguish a benign microcalcification pattern from one that is malignant. A fuzzy technique in conjunction with three features was used to detect a microcalcification pattern and a neural network was used to classify it into benign/malignant. The system was developed on a Microsoft Windows platform. It is an easy-to-use intelligent system that gives the user options to diagnose, detect, enlarge, zoom and measure distances of areas in digital mammograms.