基于多尺度最大李雅普诺夫指数的表面肌电信号模式识别

为了提高动作表面肌电信号的识别率,提出一种将最大李雅普诺夫指数和多尺度分析结合的方法。从非线性和非平稳的角度出发,引入多尺度最大李雅普诺夫指数特征,并应用到人体前臂6类动作表面肌电信号的模式识别中。首先利用希尔伯特-黄变换,对原始信号进行经验模态分解,即多尺度分解;然后利用非线性时间序列分析方法,计算多尺度最大李雅普诺夫指数;最后将多尺度最大李雅普诺夫指数作为特征向量,输入支持向量机进行识别。平均识别率达到97.5%,比利用原始信号的最大李雅普诺夫指数进行识别时提高了3.9%。结果表明,利用多尺度最大李雅普诺夫指数对动作表面肌电信号进行模式识别效果良好。     

Active Contour Based Segmentation and Classification for Pleura Diseases Based on Otsu’s Thresholding and Support Vector Machine (SVM)

Objective: Generally, lung cancer is the abnormal growth of cells that originates in one or both lungs. Finding thepulmonary nodule helps in the diagnosis of lung cancer in early stage and also increase the lifetime of the individual.Accurate segmentation of normal and abnormal portion in segmentation is challenging task in computer-aided diagnostics.Methods: The article proposes an innovative method to spot the cancer portion using Otsu’s segmentation algorithm. Itis followed by a Support Vector Machine (SVM) classifier to classify the abnormal portion of the lung image. Results:The suggested methods use the Otsu’s thresholding and active contour based segmentation techniques to locate theaffected lung nodule of CT images. The segmentation is followed by an SVM classifier in order to categorize theaffected portion is normal or abnormal. The proposed method is suitable to provide good and accurate segmentationand classification results for complex images. Conclusion: The comparative analysis between the two segmentationmethods along with SVM classifier was performed. A classification process based on active contour and SVM techniquesprovides better than Otsu’s segmentation for complex lung images.     

Computer-aided diagnosis of cervical lymph nodes on ultrasonography

A computerized system differentiating cervical lymph nodes on ultrasonography as malignant or benign was developed from a database of 210 cases. Ten quantitative features representing sonographic features of size, margin, nodal border, shape, medulla ratio, medulla distribution, echogenicity, echogeneity, vascular density, and vascular pattern, were respectively calculated under the node contour segmented by an improved snake model. A rough margin based support vector machine was trained to distinguish between malignant and benign nodes using the 10 computerized features. The receiver operating characteristic (ROC) analysis was used to evaluate the performance. The developed system showed the normalized area under the ROC curve (Az, which is used as a summarized measure of the accuracy, ranges from 0.5 to 1.0) of 0.892. Compared with the radiologist's performance of Az of 0.784 this system has the potential to be an aid to radiologists in the task of distinguishing between malignant and benign cervical nodes on ultrasonography.     

Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme

The objective of this study is to investigate the use of pattern classification methods for distinguishing different types of brain tumors, such as primary gliomas from metastases, and also for grading of gliomas. The availability of an automated computer analysis tool that is more objective than human readers can potentially lead to more reliable and reproducible brain tumor diagnostic procedures. A computer‐assisted classification method combining conventional MRI and perfusion MRI is developed and used for differential diagnosis. The proposed scheme consists of several steps including region‐of‐interest definition, feature extraction, feature selection, and classification. The extracted features include tumor shape and intensity characteristics, as well as rotation invariant texture features. Feature subset selection is performed using support vector machines with recursive feature elimination. The method was applied on a population of 102 brain tumors histologically diagnosed as metastasis ( 24 ), meningiomas ( 4 ), gliomas World Health Organization grade II ( 22 ), gliomas World Health Organization grade III ( 18 ), and glioblastomas ( 34 ). The binary support vector machine classification accuracy, sensitivity, and specificity, assessed by leave‐one‐out cross‐validation, were, respectively, 85%, 87%, and 79% for discrimination of metastases from gliomas and 88%, 85%, and 96% for discrimination of high‐grade (grades III and IV) from low‐grade (grade II) neoplasms. Multiclass classification was also performed via a one‐vs‐all voting scheme. Magn Reson Med, 2009. © 2009 Wiley‐Liss, Inc.     

机器学习方法在早产和低出生体重儿预测中的应用

应用机器学习方法构建早产儿和低出生体重儿的预测模型,包括逻辑回归、支持向量机和随机森林算法,运用交叉验证法得到不同算法的最优模型,综合准确率、F1值和AUC值评估3种模型的预测性能,结果表明基于随机森林算法的模型预测效果最好。     

基于加权自相关函数特征提取法的多类蛋白质同源寡聚体分类研究

我们提出一种新的特征提取方法,即用蛋白质序列的氨基酸组成成分和一系列的氨基酸残基指数加权自相关函数构成特征向量,表示蛋白质序列,与支持向量机算法组合对蛋白质同源二聚体、同源三聚体、同源四聚体、同源六聚体进行分类研究,得到较好的分类结果。在Jackknife检验下,采用支持向量机算法,基于此新特征提取法所构成的参数集QIANA、QIANB、MEEJ、ROBB和SNEP的总分类精度分别为77.63%、77.16%、76.46%、76.70%、75.06%,分别比传统氨基酸组成成分特征提取法(参数集为COMP)提高6.39、5.92、5.22、5.46、3.82个百分点。对于参数集QIANA,支持向量机的总分类精度为77.63%,比协方差算法提高16.29个百分点。这些结果表明:(1新特征提取法是有效和可行的,基于此特征提取法构成的特征向量包含蛋白质四级结构信息,且可能捕获了埋藏在缔合亚基作用部位接触表面的基本信息;(2)对于蛋白质同源寡聚体分类研究,支持向量机是非常有效的。     

支持向量机在早期癌症检测中的应用

支持向量机是在统计学习理论基础上发展而来的一种新的通用学习方法,较好地解决了有限样本的学习分类问题.在早期癌症诊断中,由于存在癌细胞缺乏、病人个体的特异性和数据本身的噪声等因素的影响,要进行非常准确的诊断是困难的.用支持向量机的分类算法,选取不同的核函数,构造了支持向量机的不同分类器,并将其应用于早期癌症诊断.非线性的支持向量机取得了较高的准确率,表明支持向量机在早期癌症的诊断中有很大的应用潜力.