基于主成分分析法和极限学习机的尿沉渣图像识别算法研究
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摘要
针对尿沉渣中的有形成分进行检测和分析,提出结合主成分分析(PCA)和极限学习机(ELM)的识别和统计方法。该方法通过PCA对样本进行特征提取和降维后输入到ELM进行训练,根据训练得出的模型与未经PCA处理的样本训练的模型进行检测效果对比。实验结果表明,使用PCA处理后的样本训练得出的模型具有更高的识别准确度和稳定性,同时训练时间大幅减少。
The recognition and statistics method combining principal component analysis(PCA) and extreme learning machine(ELM) is proposed to detect and analyze the visible components in urine sediment. The features of the sample are extracted by means of PCA and then input to extreme learning machine(ELM)for training after dimensionality reduction. The detection effects of the trained model got by training and sample training model without PCA processing are compared. The experimental result shows that the model obtained by sample training after PCA processing has higher recognition accuracy and stability,and its training time is greatly reduced.
The recognition and statistics method combining principal component analysis(PCA) and extreme learning machine(ELM) is proposed to detect and analyze the visible components in urine sediment. The features of the sample are extracted by means of PCA and then input to extreme learning machine(ELM)for training after dimensionality reduction. The detection effects of the trained model got by training and sample training model without PCA processing are compared. The experimental result shows that the model obtained by sample training after PCA processing has higher recognition accuracy and stability,and its training time is greatly reduced.
引文
[1]刘睿.尿沉渣图像分割与识别算法研究[D].重庆:重庆大学,2017.LIU Rui. Study on the image segmentation and recognition algorithm of urine sediment[D]. Chongqing:Chongqing University,2017.
[2]周江嫚.基于PCA-ELM的模拟电路故障诊断[J].电子科技,2017,30(5):72-75.ZHOU Jiangman. Analog circuit fault diagnosis based on PCAELM[J]. Electronic technology,2017,30(5):72-75.
[3]陈绍炜,吴敏华,赵帅.基于PCA和ELM的模拟电路故障诊断[J].计算机工程与应用,2015(11):248-251.CHEN Shaowei, WU Minhua, ZHAO Shuai. Analog circuit fault diagnosis based on PCA and ELM[J]. Computer engineering and applications,2015(11):248-251.
[4]廖建勇.尿沉渣显微图像中的管型分割与识别[D].长沙:湖南大学,2009.LIAO Jianyong. Tube segmentation and identification in microscopic image of urine sediment[D]. Changsha:Hunan University,2009.
[5]付华,王馨蕊,王志军,等.基于PCA和PSO-ELM的煤与瓦斯突出软测量研究[J].传感技术学报,2014(12):1710-1715.FU Hua,WANG Xinrui,WANG Zhijun,et al. Research on the soft sensor of coal and gas outburst based on PCA and PSOELM[J]. Chinese journal of sensors and actuators,2014(12):1710-1715.
[6]裘日辉,刘康玲,谭海龙,等.基于极限学习机的分类算法及在故障识别中的应用[J].浙江大学学报(工学版),2016(10):1965-1972.QIU Rihui,LIU Kangling,TAN Hailong,et al. Classification algorithm based on extreme learning machine and its application in fault recognition[J]. Journal of Zhejiang University(Engineering Edition),2016(10):1965-1972.
[7]于林杰.尿沉渣显微图像有形成分分割与特征提取方法研究[D].重庆:重庆大学,2016.YU Linjie. Study on the segmentation and feature extraction of visible components of urine sediment microscopic images[D].Chongqing:Chongqing University,2016.
[8] IOSIFIDIS A,TEFAS A,PITAS I. Approximate kernel extreme learning machine for large scale data classification[J].Neurocomputing,2017,219:210-220.
[9] AVCI D,LEBLEBICIOGLU M K,POYRAZ M,et al. A new method based on adaptive discrete wavelet entropy energy and neural network classifier(ADWEENN)for recognition of urine cells from microscopic images independent of rotation and scaling[J]. Journal of medical systems,2014,38(2):1-9.
[10] LI Y M,ZENG X P. A new strategy for urinary sediment segmentation based on wavelet, morphology and combination method[J]. Computer methods&programs in biomedicine,2006,84(2/3):162-173.
[11] HUANG G B,BAI Z,KASUN L L C,et al. Local receptive fields based extreme learning machine[J]. IEEE computational intelligence magazine,2015,10(2):18-29.
[12]李伟红,于林杰,龚卫国.基于非参数变换的尿沉渣细胞图像识别方法[J].仪器仪表学报,2015,36(12):2722-2730.LI Weihong,YU Linjie,GONG Weiguo. Urine sediment cell image recognition method based on non-parametric transformation[J]. Chinese journal of scientific instrument,2015,36(12):2722-2730.
[13] CHEN Kai,Lü Qi,LU Yao,et al. Robust regularized extreme learning machine for regression using iteratively reweighted least squares[J]. Neurocomputing,2017,230:345-358.
[2]周江嫚.基于PCA-ELM的模拟电路故障诊断[J].电子科技,2017,30(5):72-75.ZHOU Jiangman. Analog circuit fault diagnosis based on PCAELM[J]. Electronic technology,2017,30(5):72-75.
[3]陈绍炜,吴敏华,赵帅.基于PCA和ELM的模拟电路故障诊断[J].计算机工程与应用,2015(11):248-251.CHEN Shaowei, WU Minhua, ZHAO Shuai. Analog circuit fault diagnosis based on PCA and ELM[J]. Computer engineering and applications,2015(11):248-251.
[4]廖建勇.尿沉渣显微图像中的管型分割与识别[D].长沙:湖南大学,2009.LIAO Jianyong. Tube segmentation and identification in microscopic image of urine sediment[D]. Changsha:Hunan University,2009.
[5]付华,王馨蕊,王志军,等.基于PCA和PSO-ELM的煤与瓦斯突出软测量研究[J].传感技术学报,2014(12):1710-1715.FU Hua,WANG Xinrui,WANG Zhijun,et al. Research on the soft sensor of coal and gas outburst based on PCA and PSOELM[J]. Chinese journal of sensors and actuators,2014(12):1710-1715.
[6]裘日辉,刘康玲,谭海龙,等.基于极限学习机的分类算法及在故障识别中的应用[J].浙江大学学报(工学版),2016(10):1965-1972.QIU Rihui,LIU Kangling,TAN Hailong,et al. Classification algorithm based on extreme learning machine and its application in fault recognition[J]. Journal of Zhejiang University(Engineering Edition),2016(10):1965-1972.
[7]于林杰.尿沉渣显微图像有形成分分割与特征提取方法研究[D].重庆:重庆大学,2016.YU Linjie. Study on the segmentation and feature extraction of visible components of urine sediment microscopic images[D].Chongqing:Chongqing University,2016.
[8] IOSIFIDIS A,TEFAS A,PITAS I. Approximate kernel extreme learning machine for large scale data classification[J].Neurocomputing,2017,219:210-220.
[9] AVCI D,LEBLEBICIOGLU M K,POYRAZ M,et al. A new method based on adaptive discrete wavelet entropy energy and neural network classifier(ADWEENN)for recognition of urine cells from microscopic images independent of rotation and scaling[J]. Journal of medical systems,2014,38(2):1-9.
[10] LI Y M,ZENG X P. A new strategy for urinary sediment segmentation based on wavelet, morphology and combination method[J]. Computer methods&programs in biomedicine,2006,84(2/3):162-173.
[11] HUANG G B,BAI Z,KASUN L L C,et al. Local receptive fields based extreme learning machine[J]. IEEE computational intelligence magazine,2015,10(2):18-29.
[12]李伟红,于林杰,龚卫国.基于非参数变换的尿沉渣细胞图像识别方法[J].仪器仪表学报,2015,36(12):2722-2730.LI Weihong,YU Linjie,GONG Weiguo. Urine sediment cell image recognition method based on non-parametric transformation[J]. Chinese journal of scientific instrument,2015,36(12):2722-2730.
[13] CHEN Kai,Lü Qi,LU Yao,et al. Robust regularized extreme learning machine for regression using iteratively reweighted least squares[J]. Neurocomputing,2017,230:345-358.