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首页> 外文期刊>Journal of medical systems >An Improved Pathological Brain Detection System Based on Two-Dimensional PCA and Evolutionary Extreme Learning Machine
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An Improved Pathological Brain Detection System Based on Two-Dimensional PCA and Evolutionary Extreme Learning Machine

机译：基于二维PCA和进化极限学习机的改进病理脑检测系统

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摘要

Pathological brain detection has made notable stride in the past years, as a consequence many pathological brain detection systems (PBDSs) have been proposed. But, the accuracy of these systems still needs significant improvement in order to meet the necessity of real world diagnostic situations. In this paper, an efficient PBDS based on MR images is proposed that markedly improves the recent results. The proposed system makes use of contrast limited adaptive histogram equalization (CLAHE) to enhance the quality of the input MR images. Thereafter, two-dimensional PCA (2DPCA) strategy is employed to extract the features and subsequently, a PCA+LDA approach is used to generate a compact and discriminative feature set. Finally, a new learning algorithm called MDE-ELM is suggested that combines modified differential evolution (MDE) and extreme learning machine (ELM) for segregation of MR images as pathological or healthy. The MDE is utilized to optimize the input weights and hidden biases of single-hidden-layer feed-forward neural networks (SLFN), whereas an analytical method is used for determining the output weights. The proposed algorithm performs optimization based on both the root mean squared error (RMSE) and norm of the output weights of SLFNs. The suggested scheme is benchmarked on three standard datasets and the results are compared against other competent schemes. The experimental outcomes show that the proposed scheme offers superior results compared to its counterparts. Further, it has been noticed that the proposed MDE-ELM classifier obtains better accuracy with compact network architecture than conventional algorithms.
机译：在过去几年中，病理脑检测已经显着迈出，因此已经提出了许多病理脑检测系统（PBDS）。但是，这些系统的准确性仍然需要显着的改进，以满足现实世界诊断情况的必要性。在本文中，提出了一种基于MR图像的有效PBD，从而显着提高了最近的结果。所提出的系统利用对比度有限的自适应直方图均衡（CLAHE）来增强输入MR图像的质量。此后，采用二维PCA（2DPCA）策略来提取特征，随后，使用PCA + LDA方法来生成紧凑且辨别特征集。最后，建议称为MDE-ELM的新的学习算法，其将修改的差分演进（MDE）和极端学习机（ELM）结合以进行MR图像作为病理或健康的偏析。 MDE用于优化单隐层前馈神经网络（SLFN）的输入权重和隐藏偏差，而分析方法用于确定输出权重。该算法基于SLFN的输出权重的根均方误差（RMSE）和标准来执行优化。建议的方案在三个标准数据集上基准测试，并将结果与​​其他称职方案进行比较。实验结果表明，与其同行相比，该方案提供了卓越的结果。此外，已经注意到所提出的MDE-ELM分类器比传统算法具有紧凑的网络架构获得更好的精度。

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