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Classification of breast tissue images based on wavelet transform using discriminant analysis, neural network and SVM

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7 Author(s)
Hae-Gil Hwang ; Sch. of Comput. Eng., Inje Univ., Gimhae, South Korea ; Hyun-Ju Choi ; Byoung-Doo Kang ; Hye-Kyoung Yoon
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In this paper, we described breast tissue image analyses using texture features from Haar wavelet transformed images to classify breast lesion of ductal organ Benign, DCIS and CA. The approach for creating a classifier is composed of 2 steps: feature extraction and classification. Therefore, in the feature extraction step, we extracted texture features from wavelet transformed images with 10× magnification. In the classification step, we created three classifiers from each image of extracted features using statistical discriminant analysis, neural networks (back-propagation algorithm) and SVM (support vector machines). In this study, we conclude that the best classifier in histological sections of breast tissue in the texture features from second-level wavelet transformed images used in discriminant function.

Published in:

Enterprise networking and Computing in Healthcare Industry, 2005. HEALTHCOM 2005. Proceedings of 7th International Workshop on

Date of Conference:

23-25 June 2005