Automatic detection of breast cancers in mammograms using structured support vector machines
Publication in refereed journal


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其它資訊
摘要Breast cancer is one of the most common cancers diagnosed in women. Large margin classifiers like the support vector machine (SVM) have been reported effective in computer-assisted diagnosis systems for breast cancers. However, since the separating hyperplane determination exclusively relies on support vectors, the SVM is essentially a local classifier and its performance can be further improved. In this work, we introduce a structured SVM model to determine if each mammographic region is normal or cancerous by considering the cluster structures in the training set. The optimization problem in this new model can be solved efficiently by being formulated as one second order cone programming problem. Experimental evaluation is performed on the Digital Database for Screening Mammography (DDSM) dataset. Various types of features, including curvilinear features, texture features, Gabor features, and multi-resolution features, are extracted from the sample images. We then select the salient features using the recursive feature elimination algorithm. The structured SVM achieves better detection performance compared with a well-tested SVM classifier in terms of the area under the ROC curve. (C) 2009 Elsevier B.V. All rights reserved.
著者Wang DF, Shi L, Heng PA
期刊名稱Neurocomputing
出版年份2009
月份8
日期1
卷號72
期次13-15
出版社Elsevier
頁次3296 - 3302
國際標準期刊號0925-2312
電子國際標準期刊號1872-8286
語言英式英語
關鍵詞Breast cancer detection; Computer-assisted diagnosis; Structured support vector machine; Support vector machine
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

上次更新時間 2021-23-01 於 01:32