Microcalcification patterns recognition based combination of autoassociator and classifier

Authors:
Wencang Zhao;Xinbo Yu;Fengxiang Li
Affiliations:
College of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao, China;Department of Oral Medicine, the Affiliated Hospital of Medical College, Qingdao University, Qingdao, China;Department of Gynecology, People’s Hospital of Jimo, Qingdao, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

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Abstract

This paper presents a microcalcification patterns recognition method based autoassociator and classifier to detect the breast cancer. It studies the autoassociative and classification abilities of a neural network approach to classify the microcalcification patterns into Benign and Malignant using some certain image structure features. The proposed technique used the combination of two kinds of neural networks, autoassociator and classifier to analyze the microcalcification. It could obtain 88% classification rate for testing dataset and 100% classification rate for training dataset.