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Effective recognition of MCCs in mammograms using an improved neural classifier

Ren, J, Wang, D and Jiang, J (2011) Effective recognition of MCCs in mammograms using an improved neural classifier Engineering Applications of Artificial Intelligence, 24 (4). pp. 638-645.

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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 Az. This performance may become much worse when the training samples are imbalanced. As a result, an improvedneuralclassifier 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 F1 score and Az has fully validated the effectiveness of our proposed method for the successful classification of clustered microcalcifications.

Item Type: Article
Authors :
NameEmailORCID
Ren, JUNSPECIFIEDUNSPECIFIED
Wang, DUNSPECIFIEDUNSPECIFIED
Jiang, Jjianmin.jiang@surrey.ac.ukUNSPECIFIED
Date : 2011
Identification Number : 10.1016/j.engappai.2011.02.011
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 12:25
Last Modified : 17 May 2017 15:03
URI: http://epubs.surrey.ac.uk/id/eprint/835288

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