检索结果-内蒙古大学图书馆

Characterization of chronic liver disease based on ultrasound images using the variants of grey-level difference matrix

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE 2018年第9期232卷 884-900页

作者： Bharti, Puja Mittal, Deepti Ananthasivan, Rupa Thapar Inst Engn & Technol Dept Elect & Instrumental Engn Patiala 147004 Punjab India Manipal Hosp Dept Radiol Bangalore Karnataka India

Chronic liver diseases are fifth leading cause of fatality in developing countries. Early diagnosis is important for timely treatment and to salvage life. Ultrasound imaging is frequently used to examine abnormalities of liver. However, ambiguity lies in visual interpretation of liver stages on ultrasound images. This difficult visualization problem can be solved by analysing extracted textural features from images. Grey-level difference matrix, a texture feature extraction method, can provide information about roughness of liver surface, sharpness of liver borders and echotexture of liver parenchyma. In this article, the behaviour of variants of grey-level difference matrix in characterizing liver stages is investigated. The texture feature sets are extracted by using variants of grey-level difference matrix based on two, three, five and seven neighbouring pixels. Thereafter, to take the advantage of complementary information from extracted feature sets, feature fusion schemes are implemented. In addition, hybrid feature selection (combination of relieff filter method and sequential forward selection wrapper method) is used to obtain optimal feature set in characterizing liver stages. Finally, a computer-aided system is designed with the optimal feature set to classify liver health in terms of normal, chronic liver, cirrhosis and hepatocellular carcinoma evolved over cirrhosis. In the proposed work, experiments are performed to (1) identify the best approximation of derivative (forward, central or backward);(2) analyse the performance of individual feature sets of variants of grey-level difference matrix;(3) obtain optimal feature set by exploiting the complementary information from variants of grey-level difference matrix and (4) analyse the performance of proposed method in comparison with existing feature extraction methods. These experiments are carried out on database of 754 segmented regions of interest formed by clinically acquired ultrasound images. The res

关键词： Chronic liver disease computer-aided diagnosis grey-level difference matrix relieff algorithm sequential forward selection feature fusion

来源：评论

学校读者我要写书评

暂无评论

HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning

引用

INFORMATION SCIENCES 2013年 222卷 282-301页

作者： Zafra, Amelia Pechenizkiy, Mykola Ventura, Sebastian Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain Eindhoven Univ Technol Dept Comp Sci NL-5600 MB Eindhoven Netherlands

Feature selection techniques have been successfully applied in many applications for making supervised learning more effective and efficient. These techniques have been widely used and studied in traditional supervised learning settings, where each instance is expected to have a label. In multiple instance learning (MIL) each example or bag consists of a variable set of instances, and the label is known for the bag as a whole, but not for the individual instances it consists of. Therefore utilizing these labels for feature selection in MIL becomes less straightforward. In this paper we study a new feature subset selection method for MIL called HyDR-MI (hybrid dimensionality reduction method for multiple instance learning). The hybrid consists of the filter component based on an extension of the relieff algorithm developed for working with MIL and the wrapper component based on a genetic algorithm that optimizes the search for the best feature subset from a reduced set of features, output by the filter component. We conducted an extensive experimental evaluation of our method on five benchmark datasets and 17 classification algorithms for MIL The results of our study show the potential of the proposed hybrid with respect to the desirable effect it produces: a significant improvement of the predictive performance of-many MIL classification techniques as compared to the effect of filter-based feature selection. This is achieved due to the possibility to decide how many of the top ranked features are useful for each particular algorithm and the possibility to discard redundant attributes. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Multiple instance learning Feature selection relieff algorithm Genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Preliminary Study of Chronic Liver Classification on Ultrasound Images Using an Ensemble Model

引用

ULTRASONIC IMAGING 2018年第6期40卷 357-379页

作者： Bharti, Puja Mittal, Deepti Ananthasivan, Rupa Thapar Inst Engn & Technol Patiala 147004 Punjab India Manipal Hosp Bangalore Karnataka India

Chronic liver diseases are fifth leading cause of fatality in developing countries. Their early diagnosis is extremely important for timely treatment and salvage life. To examine abnormalities of liver, ultrasound imaging is the most frequently used modality. However, the visual differentiation between chronic liver and cirrhosis, and presence of heptocellular carcinomas (HCC) evolved over cirrhotic liver is difficult, as they appear almost similar in ultrasound images. In this paper, to deal with this difficult visualization problem, a method has been developed for classifying four liver stages, that is, normal, chronic, cirrhosis, and HCC evolved over cirrhosis. The method is formulated with selected set of "handcrafted" texture features obtained after hierarchal feature fusion. These multiresolution and higher order features, which are able to characterize echotexture and roughness of liver surface, are extracted by using ranklet, gray-level difference matrix and gray-level co-occurrence matrix methods. Thereafter, these features are applied on proposed ensemble classifier that is designed with voting algorithm in conjunction with three classifiers, namely, k-nearest neighbor (k-NN), support vector machine (SVM), and rotation forest. The experiments are conducted to evaluate the (a) effectiveness of "handcrafted" texture features, (b) performance of proposed ensemble model, (c) effectiveness of proposed ensemble strategy, (d) performance of different classifiers, and (e) performance of proposed ensemble model based on Convolutional Neural Networks (CNN) features to differentiate four liver stages. These experiments are carried out on database of 754 segmented regions of interest formed by clinically acquired ultrasound images. The results show that classification accuracy of 96.6% is obtained by use of proposed classifier model.

关键词： ensemble classifier computer-aided diagnosis chronic liver disease rotation forest (RF) ranklet transform feature fusion relieff algorithm pretrained CNN features CNN from scratch fine-tune a CNN

来源：评论

学校读者我要写书评

暂无评论

Predictive model for construction labour productivity using hybrid feature selection and principal component analysis

引用

CANADIAN JOURNAL OF CIVIL ENGINEERING 2022年第8期49卷 1366-1378页

作者： Ebrahimi, Sara Kazerooni, Matin Sumati, Vuppuluri Fayek, Aminah Robinson Univ Alberta Dept Civil & Environm Engn Edmonton AB T6G 1H9 Canada

Construction labour productivity (CLP) is affected by numerous variables made up of subjective and objective factors. Thus, CLP modelling and prediction are a complex task, leading to high computational cost and the risk of overfitting of data. This paper proposes a predictive model for CLP by integrating hybrid feature selection (HFS), as a combination of filter and wrapper methods, with principal component analysis (PCA). This developed HFS-PCA method reduces the dimensionality and complexity of CLP data and obtains better prediction performance by identifying the most predictive factors. Identified factors are utilized as inputs for various classification methods to predict CLP. Finally, prediction errors of the classification methods with and without using the proposed HFS-PCA method are compared, and the most accurate classification method is selected to develop the CLP predictive model. Experimental results show that using HFS-PCA for CLP prediction leads to better performances compared with past studies.

关键词： construction labour productivity prediction hybrid feature selection principal component analysis genetic algo-rithm support vector machine relieff algorithm

来源：评论

学校读者我要写书评

暂无评论

Efficient deep feature selection for remote sensing image recognition with fused deep learning architectures

引用

JOURNAL OF SUPERCOMPUTING 2020年第11期76卷 8413-8431页

作者： Ozyurt, Fatih Firat Univ Dept Informat Elazig Turkey

Convolutional neural networks (CNNs) have recently emerged as a popular topic for machine learning in various academic and industrial fields. It is often an important problem to obtain a dataset with an appropriate size for CNN training. However, the lack of training data in the case of remote image research leads to poor performance due to the overfitting problem. In addition, the back-propagation algorithm used in CNN training is usually very slow and thus requires tuning different hyper-parameters. In order to overcome these drawbacks, a new approach fully based on machine learning algorithm to learn useful CNN features from Alexnet, VGG16, VGG19, GoogleNet, ResNet and SqueezeNet CNN architectures is proposed in the present study. This method performs a fast and accurate classification suitable for recognition systems. Alexnet, VGG16, VGG19, GoogleNet, ResNet and SqueezeNet pretrained architectures were used as feature extractors. The proposed method obtains features from the last fully connected layers of each architecture and applies the relieff feature selection algorithm to obtain efficient features. Then, selected features are given to the support vector machine classifier with the CNN-learned features instead of the FC layers of CNN to obtain excellent results. The effectiveness of the proposed method was tested on the UC-Merced dataset. Experimental results demonstrate that the proposed classification method achieved an accuracy rate of 98.76% and 99.29% in 50% and 80% training experiment, respectively.

关键词： CNN Feature reduction relieff algorithm Image recognition UC-Merced dataset

来源：评论

学校读者我要写书评

暂无评论

Investigation of acoustic and visual features for acoustic scene classification

引用

EXPERT SYSTEMS WITH APPLICATIONS 2019年 126卷 20-29页

作者： Xie, Jie Zhu, Mingying Jiangnan Univ Sch Internet Things Engn Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Univ Ottawa Dept Econ Ottawa ON K1N 6N5 Canada

Acoustic scene classification has gained great interests in recent years due to its diverse applications. Various acoustic and visual features have been proposed and evaluated. However, few studies have investigated acoustic and visual feature aggregation for acoustic scene classification. In this paper, we investigated various feature sets based on the fusion of acoustic and visual features. Specifically, acoustic features are directly extracted from the waveform: spectral centroid, spectral entropy, spectral flux, spectral roll-off, short-time energy, zero-crossing rate, and Mel-frequency Cepstral coefficients. For visual features, we calculate local binary pattern, histogram of gradients, and moments based on the audio scene time-frequency representation. Then, three feature selection algorithms are applied to various feature sets to reduce feature dimensionality: correlation-based feature selection, principal component analysis, and relieff. Experimental results show that our proposed system was able to achieve an accuracy improvement of 15.43% compared to the baseline system with the development set. When all development sets are used for training, the performance based on the evaluation set provided by the TUT Acoustic scene 2016 challenge is 87.44%, which is the fourth best among all non-neural network systems. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Acoustic scene classification Acoustic features Visual features relieff algorithm Support vector machine

来源：评论

学校读者我要写书评

暂无评论

Detection of Alzheimer's disease using grey wolf optimization based clustering algorithm and deep neural network from magnetic resonance images

引用

DISTRIBUTED AND PARALLEL DATABASES 2022年第4期40卷 627-655页

作者： Suresha, Halebeedu Subbaraya Parthasarathy, Srirangapatna Sampathkumaran Univ Mysore PET Res Ctr Dept ECE Mysore Karnataka India PES Coll Engn Dept ECE Mandya India

The automated magnetic resonance imaging (MRI) processing techniques are gaining more importance in Alzheimer disease (AD) recognition, because it effectively diagnosis the pathology of the brain. Currently, computer aided diagnosis based on image analysis is an emerging tool to support AD diagnosis. In this research study, a new system is developed for enhancing the performance of AD recognition. Initially, the brain images were acquired from three online datasets and one real-time dataset such as AD Neuroimaging Initiative (ADNI), Minimal Interval Resonance Imaging in AD (MIRIAD), and Open Access Series of Imaging Studies (OASIS) and National Institute of Mental Health and Neuro Sciences (NIMHANS). Then, adaptive histogram equalization (AHE) and grey wolf optimization based clustering algorithm (GWOCA) were applied for denoising and segmenting the brain tissues;grey matter (GM), cerebro-spinal fluid (CSF), and white matter (WM) from the acquired images. After segmentation, the feature extraction was performed by utilizing dual tree complex wavelet transform (DTCWT), local ternary pattern (LTP) and Tamura features to extract the feature vectors from the segmented brain tissues. Then, relieff methodology was used to select the active features from the extracted feature vectors. Finally, the selected active feature values were classified into three classes [AD, normal and mild cognitive impairment (MCI)] utilizing deep neural network (DNN) classifier. From the simulation result, it is clear that the proposed framework achieved good performance in disease classification and almost showed 2.2-6% enhancement in accuracy of all four datasets.

关键词： Alzheimer disease recognition and classification Deep neural network Grey wolf optimization based clustering algorithm Histogram equalization relieff algorithm

来源：评论

学校读者我要写书评

暂无评论

Electrical energy prediction using a surface fitting model for an on-farm direct expansion bulk milk cooler (DXBMC) in South Africa

引用

JOURNAL OF ENGINEERING DESIGN AND TECHNOLOGY 2021年第3期19卷 778-794页

作者： Mhundwa, Russel Simon, Michael Univ Ft Hare Ft Hare Inst Technol Alice South Africa

Purpose This paper aims to show that a simplified surface fitting model can be efficient in determining the energy consumption during milk cooling by an on-farm direct expansion bulk milk cooler (DXBMC). The study reveals that milk volume and the temperature gradient between the room and the final milk temperature can effectively be used for predicting the energy consumption within 95% confidence bounds. Design/methodology/approach A data acquisition system comprised a Landis and Gyr E650 power meter, TMC6-HE temperature sensors, and HOBO UX120-006M 4-channel analog data logger was designed and built for monitoring of the DXBMC. The room temperature where the DXBMC is housed was measured using a TMC6-HE temperature sensor, connected to a Hobo UX120-006M four-channel analog data logger which was configured to log at one-minute intervals. The electrical energy consumed by the DXBMC was measured using a Landis and Gyr E650 meter while the volume of milk was extracted from on the farm records. Findings The results showed that the developed model can predict the electrical energy consumption of the DXBMC within an acceptable accuracy since 80% of the variation in the electrical energy consumption by the DXBMC was explained by the mathematical model. Also, milk volume and the temperature gradient between the room and final milk temperature in the BMC are primary and secondary contributors, respectively, to electrical energy consumption by the DXBMC. Based on the system that has been monitored the findings reveal that the DXBMC was operating within the expected efficiency level as evidenced by the optimized electrical energy consumption (EEC) closely mirroring the modelled EEC with a determination coefficient of 0.95. Research limitations/implications Only one system was monitored due to unavailability of funding to deploy several data acquisition systems across the country. The milk blending temperatures, effects of the insulation of the DXBMC, were not taken into account

关键词： Regression Direct expansion bulk milk cooler Milk cooling energy relieff algorithm Surface fitting model

来源：评论

学校读者我要写书评

暂无评论

Vibration-Based Terrain Classification for Autonomous Vehicles

引用

Journal of Beijing Institute of Technology 2017年第4期26卷 440-448页

作者： Kai Zhao Mingming Dong Zhiguo Wang Yanxi Han Liang Gu School of Mechanical Engineering Beijing Institute of Technology Beijing 100081 China Beijing North Vehicle Group Corporation Beijing 100072 China

A method for terrain classification based on vibration response resulted from wheel-terrain interaction is presented. Four types of terrains including sine,gravel,cement and pebble were *** vibration data were collected by two single axis accelerometers and a triaxial seat pad accelerometer,and five data sources were utilized. The feature vectors were obtained by combining features extracted from amplitude domain,frequency domain,and time-frequency domain. The relieff algorithm was used to evaluate the importance of attributes; accordingly,the optimal feature subsets were selected. Further,the predicted class was determined by fusion of outputs provided by five data sources. Finally,a voting algorithm,wherein a class with the most frequent occurrence is the predicted class,was employed. In addition,four different classifiers,namely support vector machine,k-nearest neighbors,Nave Bayes,and decision tree,were used to perform the classification and to test the proposed method. The results have shown that performances of all classifiers are ***,the proposed method is proved to be effective.

关键词： relieff algorithm terrain classification vibration voting algorithm

来源：评论

学校读者我要写书评

暂无评论

Multi-channel lung sounds intelligent diagnosis of chronic obstructive pulmonary disease

引用

BMC PULMONARY MEDICINE 2021年第1期21卷 1-13页

作者： Yu, Hui Zhao, Jing Liu, Dongyi Chen, Zhen Sun, Jinglai Zhao, Xiaoyun Tianjin Univ Acad Med Engn & Translat Med Tianjin 300072 Peoples R China Tianjin Univ Dept Biomed Engn Tianjin Key Lab Biomed Detecting Tech & Instrumen Tianjin 300072 Peoples R China Tianjin Univ Chest Hosp Tianjin 300051 Peoples R China

Background Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease that seriously threatens people's health, with high morbidity and mortality worldwide. At present, the clinical diagnosis methods of COPD are time-consuming, invasive, and radioactive. Therefore, it is urgent to develop a non-invasive and rapid COPD severity diagnosis technique suitable for daily screening in clinical practice. Results This study established an effective model for the preliminary diagnosis of COPD severity using lung sounds with few channels. Firstly, the time-frequency-energy features of 12 channels lung sounds were extracted by Hilbert-Huang transform. And then, channels and features were screened by the relieff algorithm. Finally, the feature sets were input into a support vector machine to diagnose COPD severity, and the performance with Bayes, decision tree, and deep belief network was compared. Experimental results show that high classification performance using only 4-channel lung sounds of L1, L2, L3, and L4 channels can be achieved by the proposed model. The accuracy, sensitivity, and specificity of mild COPD and moderate + severe COPD were 89.13%, 87.72%, and 91.01%, respectively. The classification performance rates of moderate COPD and severe COPD were 94.26%, 97.32%, and 89.93% for accuracy, sensitivity, and specificity, respectively. Conclusion This model provides a standardized evaluation with high classification performance rates, which can assist doctors to complete the preliminary diagnosis of COPD severity immediately, and has important clinical significance.

关键词： Hilbert-Huang tansform relieff algorithm RespiratoryDatabase @ TR Chronic obstructive pulmonary disease

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：