Mammographic Images Interpretation Using Neural-Evolutionary Approach
In this paper, we propose a hybrid approach for mammographic images interpretation in order to detect the benign and malignant anomalies. Using a neural evolutionary approach based on the Radial Basis Function neural network (RBF) and the evolutionary strategy (ES).
After applying the growing region algorithm in segmentation stage, the RBF neural network detects the suspect regions. The inference system specifies the type of the anomaly. Some of experimental results on mammographic images show the success of the proposed approach.
Keywords: Interpretation, RBF Neural Network, Evolutionary Strategy, Growing Regions, Mammographic Images
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ABOUT THE AUTHORS
Belgrana Fatima Zohra
Assistant professor, University of Ain Temouchent: CUAT, Ph.D. student in SIMPA Laboratory, University of Sciences and Technology of Oran "Mohamed BOUDIAF", USTO-MB
Benamrane Nacera
Associated Professor, SIMPA Laboratory, University of Sciences and Technology of Oran "Mohamed BOUDIAF", USTO-MB
Belgrana Fatima Zohra
Assistant professor, University of Ain Temouchent: CUAT, Ph.D. student in SIMPA Laboratory, University of Sciences and Technology of Oran "Mohamed BOUDIAF", USTO-MB
Benamrane Nacera
Associated Professor, SIMPA Laboratory, University of Sciences and Technology of Oran "Mohamed BOUDIAF", USTO-MB