Wavelet analysis for identification of lung abnormalities using artificial neural network

A. A. Ilham, Indrabayu, Rezkiana Hasanuddin, D. M. Putri
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引用次数: 3

Abstract

This research analyzed the use of daubechies wavelet as a feature extraction and confusion matrix as the principal parameter of accuracy percentage level in neural network. Detection process began with image pre-processing, lung area segmentation, feature extraction, and training phase. Classifications of the system output consisted of normal lung, pleural effusion, and pulmonary tuberculosis. Seventy five amounts of thorax samples were used as training data and thirty five thoraxes were used as test data. The experiment results showed that the decomposition at level 7 with order db6 was the best configuration for feature extraction which attained up to 91.65% of accuracy.
小波分析在人工神经网络肺异常识别中的应用
本研究分析了在神经网络中使用daubechies小波作为特征提取和混淆矩阵作为准确率百分比水平的主要参数。检测过程从图像预处理、肺面积分割、特征提取、训练阶段开始。系统输出的分类包括正常肺、胸腔积液和肺结核。75个胸腔样本作为训练数据,35个胸腔样本作为测试数据。实验结果表明,db6阶7级分解是特征提取的最佳配置,提取准确率可达91.65%。
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