Performance Improved Iteration-Free Artificial Neural Networks for Abnormal Magnetic Resonance Brain Image Classification
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- 作者：D. Jude Hemanth ; C.Kezi Selva Vijila ; A.Immanuel Selvakumar ; J. Anitha
- 关键词：Artificial Neural Networks ; Iteration-free ; Image Classification ; Convergence rate ; Accuracy
- 刊名：Neurocomputing
- 出版年：23 April, 2014
- 年：2014
- 卷：130
- 期：Complete
- 页码：98-107
- 全文大小：636 K
文摘
Image classification is one of the typical computational applications widely used in the medical field especially for abnormality detection in Magnetic Resonance (MR) brain images. The automated image classification systems used for such applications must be significantly efficient in terms of accuracy since false detection may lead to fatal results. Another requirement is the high convergence rate which accounts for the practical feasibility of the system. Among the automated systems, Artificial Neural Network (ANN) is gaining significant positions for solving computational problems. Besides multiple advantages, there are also few drawbacks associated with the neural networks which are unnoticed for most of the applications. The main drawback is that the ANN which yields high accuracy requires high convergence time period and the ANN which are much quicker are usually inaccurate. Hence, there is a significant necessity for ANN which satisfies the criteria of high convergence rate and accuracy simultaneously. In this work, this drawback is tackled by proposing two novel neural networks namely Modified Counter Propagation Neural Network (MCPN) and Modified Kohonen Neural Network (MKNN). These networks are framed by performing modifications in the training methodology of conventional CPN and Kohonen networks. The main concept of this work is to make the ANN iteration-free which ultimately improves the convergence rate besides yielding accurate results. The performance of these networks are analysed in the context of abnormal brain image classification. Experimental results show promising results for the proposed networks in terms of the performance measures.