Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians ar...Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of melanoma.These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease.The trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reflections,air bubbles,and oils on almost all images.Active contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular shapes.An entropy and morphology-based automated mask selection is pro-posed for the active contour method.The proposed method can improve the overall segmentation along with the boundary of melanoma images.In this study,features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been combined.Therefore,hybrid fused texture features;such as local,color feature as global,shape features,and Artificial neural network(ANN)as classifiers have been proposed for the categorization of the malignant and non-malignant.Experimentations had been carried out on datasets Dermis,DermQuest,and PH2.The results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.展开更多
粒度支持向量机(granular support vector machine,GSVM)引入粒计算的方式对原始数据集进行粒度划分以提高支持向量机(support vector machine,SVM)的学习效率。传统GSVM采用静态粒划分机制,即通过提取划分后数据簇中的代表信息进行模...粒度支持向量机(granular support vector machine,GSVM)引入粒计算的方式对原始数据集进行粒度划分以提高支持向量机(support vector machine,SVM)的学习效率。传统GSVM采用静态粒划分机制,即通过提取划分后数据簇中的代表信息进行模型训练,有效地提升了SVM的学习效率,但由于GSVM对信息无差别的粒度划分导致对距离超平面较近的强信息粒提取不足,距离超平面较远的弱信息粒被过多保留,影响了SVM的学习性能。针对这一问题,本文提出了采用划分融合双向控制的粒度支持向量机方法(division-fusion support vec-tor machine,DFSVM)。该方法通过动态数据划分融合的方式,选取超平面附近的强信息粒进行深层次的划分,同时将距离超平面较远的弱信息粒进行选择性融合,以动态地保持训练样本规模的稳定性。通过实验表明,采用划分融合的方法能够在保证模型训练精度的条件下显著提升SVM的学习效率。展开更多
Several meta-analyses were recently conducted in attempts to identify the core brain regions exhibiting pathological changes in schizophrenia,which could potentially act as disease markers.Based on the findings of the...Several meta-analyses were recently conducted in attempts to identify the core brain regions exhibiting pathological changes in schizophrenia,which could potentially act as disease markers.Based on the findings of these meta-analyses,we developed a multivariate pattern analysis method to classify schizophrenic patients and healthy controls using structural magnetic resonance imaging(sMRI)data.Independent component analysis(ICA)was used to decompose gray matter density images into a set of spatially independent components.Spatial multiple regression of a region of interest(ROI)mask with each of the components was then performed to determine pathological patterns,in which the voxels were taken as features for classification.After dimensionality reduction using principal component analysis(PCA),a nonlinear support vector machine(SVM)classifier was trained to discriminate schizophrenic patients from healthy controls.The performance of the classifier was tested using a 10-fold cross-validation strategy.Experimental results showed that two distinct spatial patterns displayed discriminative power for schizophrenia,which mainly included the prefrontal cortex(PFC)and subcortical regions respectively.It was found that simultaneous usage of these two patterns improved the classification performance compared to using either of them alone.Moreover,the two pathological patterns constitute a prefronto-subcortical network,suggesting that schizophrenia involves abnormalities in networks of brain regions.展开更多
文摘Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of melanoma.These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease.The trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reflections,air bubbles,and oils on almost all images.Active contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular shapes.An entropy and morphology-based automated mask selection is pro-posed for the active contour method.The proposed method can improve the overall segmentation along with the boundary of melanoma images.In this study,features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been combined.Therefore,hybrid fused texture features;such as local,color feature as global,shape features,and Artificial neural network(ANN)as classifiers have been proposed for the categorization of the malignant and non-malignant.Experimentations had been carried out on datasets Dermis,DermQuest,and PH2.The results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.
文摘粒度支持向量机(granular support vector machine,GSVM)引入粒计算的方式对原始数据集进行粒度划分以提高支持向量机(support vector machine,SVM)的学习效率。传统GSVM采用静态粒划分机制,即通过提取划分后数据簇中的代表信息进行模型训练,有效地提升了SVM的学习效率,但由于GSVM对信息无差别的粒度划分导致对距离超平面较近的强信息粒提取不足,距离超平面较远的弱信息粒被过多保留,影响了SVM的学习性能。针对这一问题,本文提出了采用划分融合双向控制的粒度支持向量机方法(division-fusion support vec-tor machine,DFSVM)。该方法通过动态数据划分融合的方式,选取超平面附近的强信息粒进行深层次的划分,同时将距离超平面较远的弱信息粒进行选择性融合,以动态地保持训练样本规模的稳定性。通过实验表明,采用划分融合的方法能够在保证模型训练精度的条件下显著提升SVM的学习效率。
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.61003202,90820304,and 60835005)the National Basic Research Program of China(No.2011CB707802).
文摘Several meta-analyses were recently conducted in attempts to identify the core brain regions exhibiting pathological changes in schizophrenia,which could potentially act as disease markers.Based on the findings of these meta-analyses,we developed a multivariate pattern analysis method to classify schizophrenic patients and healthy controls using structural magnetic resonance imaging(sMRI)data.Independent component analysis(ICA)was used to decompose gray matter density images into a set of spatially independent components.Spatial multiple regression of a region of interest(ROI)mask with each of the components was then performed to determine pathological patterns,in which the voxels were taken as features for classification.After dimensionality reduction using principal component analysis(PCA),a nonlinear support vector machine(SVM)classifier was trained to discriminate schizophrenic patients from healthy controls.The performance of the classifier was tested using a 10-fold cross-validation strategy.Experimental results showed that two distinct spatial patterns displayed discriminative power for schizophrenia,which mainly included the prefrontal cortex(PFC)and subcortical regions respectively.It was found that simultaneous usage of these two patterns improved the classification performance compared to using either of them alone.Moreover,the two pathological patterns constitute a prefronto-subcortical network,suggesting that schizophrenia involves abnormalities in networks of brain regions.
