WBCD breast cancer database classification applying artificial metaplasticity neural network
WBCD breast cancer database classification applying artificial metaplasticity neural network
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DOI：
10.1016/j.eswa.2011.01.167
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发表时间：
2011-08-01
影响因子：
8.5
通讯作者：
Andina, D.
中科院分区：
文献类型：
--
作者：
Marcano-Cedeno, A.;Quintanilla-Dominguez, J.;Andina, D.
乳腺癌的正确诊断是医学领域的主要问题之一。从文献中发现，不同的模式识别技术可以帮助他们在这个领域取得进步。这些技术可以帮助医生形成第二意见并做出更好的诊断。在本文中，我们提出了用于模式分类的神经网络训练的新颖改进。所提出的训练算法受到神经元的生物可塑性特性和香农信息论的启发。在训练阶段，人工塑性多层感知器 (AMMLP) 算法优先更新较不频繁激活的权重，而不是较频繁激活的权重。通过这种方式，可以对化塑性进行人工建模。 AMMLP 实现了更有效的训练，同时保持了 MLP 性能。为了测试所提出的算法，我们使用了威斯康星州乳腺癌数据库 (WBCD)。 AMMLP 性能使用分类准确性、敏感性和特异性分析以及混淆矩阵进行测试。获得的 AMMLP 分类准确率为 99.26%，与反向传播算法 (BPA) 和应用于同一数据库的最新分类技术相比，这是一个非常有希望的结果。 (C) 2011 Elsevier Ltd. 保留所有权利。
The correct diagnosis of breast cancer is one of the major problems in the medical field. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. These techniques can help doctors form a second opinion and make a better diagnosis. In this paper we present a novel improvement in neural network training for pattern classification. The proposed training algorithm is inspired by the biological metaplasticity property of neurons and Shannon's information theory. During the training phase the Artificial metaplasticity Multilayer Perceptron (AMMLP) algorithm gives priority to updating the weights for the less frequent activations over the more frequent ones. In this way metaplasticity is modeled artificially. AMMLP achieves a more effcient training, while maintaining MLP performance. To test the proposed algorithm we used the Wisconsin Breast Cancer Database (WBCD). AMMLP performance is tested using classification accuracy, sensitivity and specificity analysis, and confusion matrix. The obtained AMMLP classification accuracy of 99.26%, a very promising result compared to the Backpropagation Algorithm (BPA) and recent classification techniques applied to the same database. (C) 2011 Elsevier Ltd. All rights reserved.