Ren, J. C. and Wang, D. and Jiang, J. M. (2011) Effective recognition of MCCs in mammograms using an improved neural classifier. Engineering Applications of Artificial Intelligence, 24 (4). pp. 638-645. ISSN 0952-1976Full text not available in this repository. (Request a copy from the Strathclyde author)
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.
|Keywords:||optimized decision making, breast-cancer, mammography, IMBA, digital mammography, artificial neural network, feature-extraction, image retrieval, clustered microcalcifications, balanced learning, microcalcification clusters (MCC), computer-aided diagnosis, effective recognition, mcc's, mammograms, improved, neural classifier, Engineering design, Artificial Intelligence, Control and Systems Engineering, Electrical and Electronic Engineering|
|Subjects:||Technology > Engineering (General). Civil engineering (General) > Engineering design|
|Department:||Faculty of Engineering > Design, Manufacture and Engineering Management|
|Depositing user:||Pure Administrator|
|Date Deposited:||25 Jul 2012 12:44|
|Last modified:||07 Jan 2017 01:18|