A Better Classifier Based on Rough Set and Neural Network for Medical Images

Authors:
Jiang Yun;Li Zhanhuai;Wang Yong;Zhang Longbo
Affiliations:
Northwestern Polytechnical University;Northwestern Polytechnical University;Northwestern Polytechnical University;Northwestern Polytechnical University
Venue:
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Year:
2006

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Cited 1

Rough sets and near sets in medical imaging: a review

IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks

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Abstract

Detecting tumor in mammography is a difficult task because of complexity in the image. This brings the necessity of creating automatic tools to find whether a mammography present tumor or not. In this paper we integrate neural network with reduction of rough set theory which we call the rough neural network (RNN) to classify digital mammography. The experimental results show that the RNN performs better than purely using neural network in terms of time, and it can get 92.37% classifying accuracy which is higher than 81.25% using neural network only.