Effective recognition of MCCs in mammograms using an improved neural classifier

Authors:
Jinchang Ren;Dong Wang;Jianmin Jiang
Affiliations:
Centre for Excellence in Signal and Image Processing, Department of Electronics and Electrical Engineering, University of Strathclyde, Glasgow, UK;Digital Media and Systems Research Institute, University of Bradford, Bradford, UK;Digital Media and Systems Research Institute, University of Bradford, Bradford, UK
Venue:
Engineering Applications of Artificial Intelligence
Year:
2011

Citing 15
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ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging

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Abstract

Computer-aided diagnosis is one of the most important engineering applications of artificial intelligence. In this paper, early detection of breast cancer through classification of microcalcification clusters from mammograms is emphasized. Although artificial neural network (ANN) has been widely applied in this area, the average accuracy achieved is only around 80% in terms of the area under the receiver operating characteristic curve A"z. This performance may become much worse when the training samples are imbalanced. As a result, an improved neural classifier is proposed, in which balanced learning with optimized decision making are introduced to enable effective learning from imbalanced samples. When the proposed learning strategy is applied to individual classifiers, the results on the DDSM database have demonstrated that the performance from has been significantly improved. An average improvement of more than 10% in the measurements of F"1 score and A"z has fully validated the effectiveness of our proposed method for the successful classification of clustered microcalcifications.