ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging
ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging
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DOI：
10.1016/j.knosys.2011.07.016
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发表时间：
2012-02-01
影响因子：
8.8
通讯作者：
Ren, Jinchang
中科院分区：
文献类型：
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作者：
Ren, Jinchang
微钙化团的分类在计算机辅助乳腺癌早期诊断中起着至关重要的作用，支持向量机和人工神经网络是两种常用的方法。虽然一些工作表明支持向量机的性能优于人工神经网络，但就接收器工作特性曲线Az下的面积而言，所获得的平均精度仅为80%左右。当训练样本不平衡时，这种性能可能会变得更差。因此，提出了一种新的策略，即优化决策的均衡学习策略，以实现对不平衡样本的有效学习，并将其用于评估ANN和支持向量机在这种情况下的性能。将所提出的学习策略应用于单个分类器，在DDSM数据库上的结果表明，ANN和支持向量机的性能都有了显著的提高。虽然在没有均衡学习的情况下，ANN的性能优于支持向量机，但当均衡学习和优化决策同时使用时，两种分类器的性能变得非常接近。因此，两个分类器在F-1分数和Az测量上的平均改善超过10%。这充分验证了我们提出的方法对于成功地对簇状微钙化进行分类的有效性。(C)2011爱思唯尔B.V.保留所有权利。
Classification of microcalcification clusters from mammograms plays essential roles in computer-aided diagnosis for early detection of breast cancer, where support vector machine (SVM) and artificial neural network (ANN) are two commonly used techniques. Although some work suggest that SVM performs better than ANN, 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, a new strategy namely balanced learning with optimized decision making is proposed to enable effective learning from imbalanced samples, which is further employed to evaluate the performance of ANN and SVM in this context. When the proposed learning strategy is applied to individual classifiers, the results on the DDSM database have demonstrated that the performance from both ANN and SVM has been significantly improved. Although ANN outperforms SVM when balanced learning is absent, the performance from the two classifiers becomes very comparable when both balanced learning and optimized decision making are employed. Consequently, an average improvement of more than 10% in the measurements of F-1 score and Az measurement are achieved for the two classifiers. This has fully validated the effectiveness of our proposed method for the successful classification of clustered microcalcifications. (C) 2011 Elsevier B.V. All rights reserved.