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首页> 外文期刊>Multimedia Tools and Applications >Development of pathological brain detection system using Jaya optimized improved extreme learning machine and orthogonal ripplet-Ⅱ transform
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Development of pathological brain detection system using Jaya optimized improved extreme learning machine and orthogonal ripplet-Ⅱ transform

机译：使用Jaya优化的改进型极限学习机和正交波纹-Ⅱ变换开发病理性脑部检测系统

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Pathological brain detection systems (PBDSs) have drawn much attention from researchers over the past two decades because of their significance in taking correct clinical decisions. In this paper, an efficient PBDS based on MR images is introduced that markedly improves the recent results. The proposed system makes use of contrast limited adaptive histogram equalization (CLAHE) and orthogonal discrete ripplet-II transform (O-DR2T) with degree 2 to enhance the quality of the input MR images and extract the features respectively. Subsequently, relevant features are obtained using PCA+LDA approach. Finally, a novel learning algorithm called IJaya-ELM is proposed that combines improved Jaya algorithm (IJaya) and extreme learning machine (ELM) for segregation of MR images as pathological or healthy. The improved Jaya algorithm is utilized to optimize the input weights and hidden biases of single-hidden-layer feedforward neural networks (SLFN), whereas one analytical method is used for determining the output weights. The proposed algorithm performs optimization according to both the root mean squared error (RMSE) and the norm of the output weights of SLFNs. Extensive experiments are carried out using three benchmark datasets and the results are compared against other competent schemes. The experimental results demonstrate that the proposed scheme brings potential improvements in terms of classification accuracy and number of features. Moreover, the proposed IJaya-ELM classifier achieves higher accuracy and obtains compact network architecture compared to conventional ELM and BPNN classifier.
机译：过去二十年来，病理性脑检测系统（PBDS）引起了研究人员的广泛关注，因为它们在做出正确的临床决策方面具有重要意义。在本文中，介绍了一种基于MR图像的有效PBDS，它显着改善了最近的结果。该系统利用对比度受限的自适应直方图均衡（CLAHE）和2级正交离散波纹II变换（O-DR2T）来增强输入MR图像的质量并分别提取特征。随后，使用PCA + LDA方法获得相关功能。最后，提出了一种新颖的学习算法IJaya-ELM，该算法结合了改进的Jaya算法（IJaya）和极限学习机（ELM）来分离MR图像，无论是病理性的还是健康的。改进的Jaya算法用于优化单隐藏前馈神经网络（SLFN）的输入权重和隐藏偏差，而一种分析方法用于确定输出权重。所提出的算法根据均方根误差（RMSE）和SLFN的输出权重范数进行优化。使用三个基准数据集进行了广泛的实验，并将结果与​​其他有效方案进行了比较。实验结果表明，该方案在分类准确性和特征数量方面带来了潜在的改进。此外，与传统的ELM和BPNN分类器相比，所提出的IJaya-ELM分类器实现了更高的精度并获得了紧凑的网络架构。

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