Earlier detection of cancer regions from MR image features and SVM classifiers

In this article, we examine the use of several segmentation algorithms for medical image classification. This work detects the cancer region from magnetic resonance (MR) images in earlier stage. This is accomplished in three stages. In first stage, four kinds of region‐based segmentation techniques are used such as K‐means clustering algorithm, expectation–maximization algorithm, partial swarm optimization algorithm, and fuzzy c‐means algorithm. In second stage, 18 texture features are extracting using gray level co‐occurrence matrix (GLCM). In stage three, classification is based on multi‐class support vector machine (SVM) classifier. Finally, the performance analysis of SVM classifier is analyzed using the four types of segmentation algorithm for a group of 200 patients (32—Glioma, 32—Meningioma, 44—Metastasis, 8—Astrocytoma, 72—Normal). The experimental results indicate that EM is an efficient segmentation method with 100% accuracy. In SVM, quadratic and RBF (σ = 0.5) kernel methods provide the highest classification accuracy compared to all other SVM kernel methods. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 196–208, 2016     

一种基于核主成分分析和组合分类器的虹膜识别方法

提出了一种新的虹膜特征提取与识别方法,该方法利用核主成分分析(KPCA)在高维空间具有较强的特征选择能力来提取虹膜图像的纹理特征。采用了一种距离度量和支持向量机相结合的两级分类方法,前级采用欧式距离来度量图像间的相似性,若符合条件,给出分类结果,否则拒绝,并转入后一级分类器——支持向量机分类,以减少进入支持向量机的样本数目,该组合分类方法充分利用了支持向量机识别率高和距离度量速度快的优点。实验结果表明,该方法提高了虹膜识别率,是一种有效的虹膜识别方法。     

Magnetic resonance brain image classification based on weighted‐type fractional Fourier transform and nonparallel support vector machine

To classify brain images into pathological or healthy is a key pre‐clinical state for patients. Manual classification is tiresome, expensive, time‐consuming, and irreproducible. In this study, we aimed to present an automatic computer‐aided system for brain‐image classification. We used 90 T2‐weighted images obtained by magnetic resonance images. First, we used weighted‐type fractional Fourier transform (WFRFT) to extract spectrums from each magnetic resonance image. Second, we used principal component analysis (PCA) to reduce spectrum features to only 26. Third, those reduced spectral features of different samples were combined and were fed into support vector machine (SVM) and its two variants: generalized eigenvalue proximal SVM and twin SVM. A 5 × 5‐fold cross‐validation results showed that this proposed “WFRFT + PCA + generalized eigenvalue proximal SVM” yielded sensitivity of 99.53%, specificity of 92.00%, precision of 99.53%, and accuracy of 99.11%, which are comparable with the proposed “WFRFT + PCA + twin SVM” and better than the proposed “WFRFT + PCA + SVM.” Besides, all three proposed methods were superior to eight state‐of‐the‐art algorithms. Thus, WFRFT is effective, and the proposed methods can be used in practical. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 317–327, 2015     

非均衡训练集过采样的印刷套准识别方法

目的 针对印刷标志图像训练数据集非均衡性导致印刷标志图像中少类数据套准状态识别准确率低的问题,提出改进的SMOTE训练集过采样方法,以提高少类数据的识别准确率。方法 提取印刷标志图像灰度行程矩阵的纹理特征,组成多维的模型输入特征数据。基于少类样本的邻域信息,得到少类样本的过采样参数。对少类样本采取不同的过采样策略,实现训练集样本的均衡。使用均衡的训练集建立支持向量机模型,实现对印刷套准状态的识别。结果 实验结果表明,文中方法在不同非均衡印刷数据集上,获得的平均分类准确率几何平均数Gmean为0.8507,召回率Re为0.7192,ROC曲线下面积A为0.8549。结论 文中方法在不同非均衡印刷套准数据集上的分类性能要优于实验中的SMOTE,IS和SVM等方法。     

Deep transfer learning based hepatitis B virus diagnosis using spectroscopic images

Accurate identification of Hepatitis B virus (HBV) disease by analyzing the Raman spectroscopic images is a challenge for pathologists. To save precious human lives, an efficient technique is required with higher diagnostic accuracy at early‐stage of HBV. We proposed a novel method of HBV diagnosis using deep neural networks with the concept of transfer learning and Raman spectroscopic images. The proposed approach developed by utilizing pretrained convolutional neural networks ResNet101 by employing transfer learning on a real dataset of HBV‐infected blood plasma samples. Dataset consists of 1000 Raman images in which 600 are HBV‐infected blood plasma samples, and 400 are healthy ones. The developed model is capable to detect minute variation between infected and healthy samples and achieved enhanced performance. The proposed approach has been assessed and attained high classification accuracy, sensitivity, specificity, and AUC of 99.7%, 100%, 99.25%, and 98.7%, respectively. The proposed TL‐ResNet101 model outperformed the conventional approaches such as PCA‐SVM and PCA‐LDA and demonstrated improved accuracy more than 7%. High performance indicates that the developed TL‐ResNet101 model has potential to use for HBV diagnosis. Moreover, the developed automated approach can be extended for other disease.     

基于商空间和支持向量机的滚动轴承故障智能诊断

针对滚动轴承故障诊断中存在的故障类型多样且有效特征难以选取等问题,提出了一种基于商空间和支持向量机的智能诊断模型。该模型利用商空间的粒化分层思想将输入样本按照不同等价关系进行粒化分层,并对每一粒度层的时域和频域特征进行约简,然后将每一层约简后的特征向量输入到支持向量机进行状态识别,最后对各粒度层状态识别结果加权融合得到最终结果。利用轴承全寿命试验数据对该模型进行验证,识别精度达到96.92%。     

Ensemble Support Vector Machine Algorithm for Reliability Estimation of a Mining Machine

In this study, a support vector machine (SVM)‐based ensemble model was developed for reliability forecasting. The hyperparameters of the SVM were selected by applying a genetic algorithm. Input variables of the SVM model were selected by maximizing the mean entropy value. The diverse members of the ensemble model were obtained by a k‐means clustering algorithm, and one ensemble member was selected from each cluster by choosing the closest from the cluster center. The optimum cluster number was selected using the Davies–Bouldin index. The developed model was validated by a benchmark turbocharger data set. A comparative study reveals that the proposed method performs better than existing methods on benchmark data sets. A case study was conducted investigating a dumper operated at a coal mine in India. Time‐to‐failure historical data for the dumper were collected, and cumulative time to failure was calculated for reliability forecasting. Study results demonstrate that the developed model performs well with high accuracy (R² = 0.97) in the prediction of dumper failure, and a comparison with other methods demonstrates the superiority of the proposed ensemble SVM model. Copyright © 2014 John Wiley & Sons, Ltd.     

基于多元图像特征分析的印刷单元故障诊断方法

目的提出一种基于画面分析的机械故障诊断方法,用于印刷机械中印刷单元的故障模式分类。方法通过印刷特定图像,获取印刷单元在正常与故障状态下的印刷画面,从网点覆盖率、灰度特征和画面纹理特征等3个方面构建表征印刷单元状态的多元图像特征集,并通过SVM构建故障识别网络。结果利用多元统计方法分析了印刷单元6类故障与特征集的映射关系,依靠画面特征实现了故障类别判断;经过实际故障诊断试验验证,所提出方法的准确率可达90%以上。结论图像特征集对于印刷机故障有着很好的分类表征能力,为印刷机维护提供了新的理论和方法。     

Diagnosis of a dermatological lesion using intelligent feature selection technique

The content-based image retrieval (CBIR) in dermatological diagnosis context, the information matching is the major concern in terms of feature vector-based classification. The discrimination of the feature vector leads to better classification as well as retrieval rate. Better retrieval results help the dermatologist to improve the diagnosis. In this paper, we proposed a support vector machine weight map (SVM W-Map)-based feature selection along with multi-class particle swarm optimization (PSO) presented for multi-class dermatological imaging dataset. The performance of the system was tested on a dataset including 1450 images and obtained 99.7% for specificity and 95.89% for sensitivity. The analysis and evaluations of results show that the proposed system has higher diagnosis ability when compared with other works.     

Classification and Research of Skin Lesions Based on Machine Learning

Classification of skin lesions is a complex identification challenge. Due to the wide variety of skin lesions, doctors need to spend a lot of time and effort to judge the lesion image which zoomed through the dermatoscopy. The diagnosis which the algorithm of identifying pathological images assists doctors gets more and more attention. With the development of deep learning, the field of image recognition has made longterm progress. The effect of recognizing images through convolutional neural network models is better than traditional image recognition technology. In this work, we try to classify seven kinds of lesion images by various models and methods of deep learning, common models of convolutional neural network in the field of image classification include ResNet, DenseNet and SENet, etc. We use a fine-tuning model with a multi-layer perceptron, by training the skin lesion model, in the validation set and test set we use data expansion based on multiple cropping, and use five models’ ensemble as the final results. The experimental results show that the program has good results in improving the sensitivity of skin lesion diagnosis.     

基于深度度量学习的轴承故障诊断方法

针对机械大数据因故障类内离散度和类间相似度较大而导致诊断精度低的问题,提出一种深度度量学习故障诊断方法,采用深度神经网络(Deep Neural Network, DNN)对故障特征进行自适应提取,并利用基于欧氏距离的边际Fisher分析(Marginal Fisher Analysis, MFA)方法进行了优选,在构建的深度度量网络(Deep Metric Network, DMN)顶层特征输出层添加BPNN(Back Propagation Neural Network, BPNN)分类器对网络参数进行微调,并实现故障的分类识别。通过对不同类型和严重程度的轴承故障进行了诊断分析,验证了该方法可以有效地对轴承故障进行高精度诊断,效果优于传统深度信念网络(Deep Belief Network, DBN)故障诊断方法以及常用时域统计特征结合支持向量机(Support Vector Machine, SVM)分类的故障诊断方法。     

MRI brain image classification—a hybrid approach

The present article proposes a novel computer‐aided diagnosis (CAD) technique for the classification of the magnetic resonance brain images. The current method adopt color converted hybrid clustering segmentation algorithm with hybrid feature selection approach based on IGSFFS (Information gain and Sequential Forward Floating Search) and Multi‐Class Support Vector Machine (MC‐SVM) classifier technique to segregate the magnetic resonance brain images into three categories namely normal, benign and malignant. The proposed hybrid evolutionary segmentation algorithm which is the combination of WFF(weighted firefly) and K‐means algorithm called WFF‐K‐means and modified cuckoo search (MCS) and K‐means algorithm called MCS‐K‐means, which can find better cluster partition in brain tumor datasets and also overcome local optima problems in K‐means clustering algorithm. The experimental results show that the performance of the proposed algorithm is better than other algorithms such as PSO‐K‐means, color converted K‐means, FCM and other traditional approaches. The multiple feature set comprises color, texture and shape features derived from the segmented image. These features are then fed into a MC‐SVM classifier with hybrid feature selection algorithm, trained with data labeled by experts, enabling the detection of brain images at high accuracy levels. The performance of the method is evaluated using classification accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) curves. The proposed method provides highest classification accuracy of greater than 98% with high sensitivity and specificity rates of greater than 95% for the proposed diagnostic model and this shows the promise of the approach. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 226–244, 2015     

A support vector machine integrated system for the classification of operation anomalies in nuclear components and systems

A support vector machine (SVM) approach to the classification of transients in nuclear power plants is presented. SVM is a machine-learning algorithm that has been successfully used in pattern recognition for cluster analysis. In the present work, single- and multiclass SVM are combined into a hierarchical structure for distinguishing among transients in nuclear systems on the basis of measured data. An example of application of the approach is presented with respect to the classification of anomalies and malfunctions occurring in the feedwater system of a boiling water reactor. The data used in the example are provided by the HAMBO simulator of the Halden Reactor Project.     

基于内积延拓LMD及SVM的轴承故障诊断方法研究

针对特征提取中局域均值分解(Local Mean Decomposition, LMD)存在端点效应缺陷及模式识别中人工神经网络(Artificial Neural Network, ANN)存在收敛速度慢、过学习等不足,提出基于内积延拓LMD及支持向量机(Support Vector Machine, SVM)的轴承故障诊断方法。利用内积延拓LMD方法对信号延拓分解抑制LMD端点效应;利用分解的可描述信号特性主分量PF(Product Function)构建初始特征向量矩阵;用SVD(Singular Value Decomposition)方法对初始特征向量矩阵进行奇异值分解,获得信号特征参数作为SVM的输入进行训练;对训练的SVM进行测试及模式分类。通过实际轴承故障信号分析及故障类型分类表明,该方法不仅能抑制LMD端点效应缺陷,且在故障模式识别中能有效避免ANN网络结构难确定、收敛速度慢等不足,能较好实现轴承故障准确分类,可用于轴承故障诊断。     

Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and non-seizure. In addition, a fast Walsh-Hadamard Transform (FWHT) technique is implemented to analyze the EEG signals within the recurrence space of the brain. Thus, Hadamard coefficients of the EEG signals are obtained via the FWHT. Moreover, the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings. Also, a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers. The LSTM classifier provides the best performance, with a testing accuracy of 99.00%. The training and testing loss rates for the LSTM are 0.0029 and 0.0602, respectively, while the weighted average precision, recall, and F1-score for the LSTM are 99.00%. The results of the SVM classifier in terms of accuracy, sensitivity, and specificity reached 91%, 93.52%, and 91.3%, respectively. The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s, respectively. The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals. Eventually, the proposed classifiers provide high classification accuracy compared to previously published classifiers.     

基于MFCC和SVM的车窗电机异常噪声辨识方法研究

为提高车窗电机异常噪声特征提取的有效性及分类识别的准确性,提出一种以优化的梅尔频率倒谱系数(Mel Frequency Cepstrum Coefficient,MFCC)为特征值,以支持向量机(Support Vector Machine,SVM)为噪声辨识模型的电机异常噪声辨识方法。在MFCC提取方法基础上,针对频谱泄漏,用Hanning自卷积窗代替Hanning窗,获得优化的MFCC,并将其作为特征值输入到SVM进行异常噪声辨识。为提高SVM判别准确率,采用人工蜂群算法实现SVM参数选择优化。实验结果表明,该方法能够有效判别电机是否存在异响,准确率达到91%。     

Feature selection and fault‐severity classification–based machine health assessment methodology for point machine sliding‐chair degradation

In this paper, we propose an offline and online machine health assessment (MHA) methodology composed of feature extraction and selection, segmentation‐based fault severity evaluation, and classification steps. In the offline phase, the best representative feature of degradation is selected by a new filter‐based feature selection approach. The selected feature is further segmented by utilizing the bottom‐up time series segmentation to discriminate machine health states, ie, degradation levels. Then, the health state fault severity is extracted by a proposed segment evaluation approach based on within segment rate‐of‐change (RoC) and coefficient of variation (CV) statistics. To train supervised classifiers, a priori knowledge about the availability of the labeled data set is needed. To overcome this limitation, the health state fault‐severity information is used to label (eg, healthy, minor, medium, and severe) unlabeled raw condition monitoring (CM) data. In the online phase, the fault‐severity classification is carried out by kernel‐based support vector machine (SVM) classifier. Next to SVM, the k‐nearest neighbor (KNN) is also used in comparative analysis on the fault severity classification problem. Supervised classifiers are trained in the offline phase and tested in the online phase. Unlike to traditional supervised approaches, this proposed method does not require any a priori knowledge about the availability of the labeled data set. The proposed methodology is validated on infield point machine sliding‐chair degradation data to illustrate its effectiveness and applicability. The results show that the time series segmentation‐based failure severity detection and SVM‐based classification are promising.     

3d discrete wavelet transform for computer aided diagnosis of Alzheimer's disease using t1‐weighted brain MRI

Early and antemortem diagnosis of Alzheimer's disease (AD) may help in the development of appropriate treatment and in slowing down the disease progression. In this work, a three‐phase computer aided approach is suggested for classification of AD patients and controls using T1‐weighted MRI. In the first phase, smoothed modulated gray matter (GM) probability maps are obtained from T1‐weighted MRIs. In the second phase, 3D discrete wavelet transform is applied on GM of five brain regions, which are well‐documented regions affected in AD, to construct features. In the third phase, a minimal set of relevant and nonredundant features are obtained using Fisher's discriminant ratio and minimum redundancy maximum relevance feature selection methods. To check the efficacy of the proposed approach, experiments were carried out on three datasets derived from the publicly available OASIS database, using three commonly used classifiers. The performance of the proposed approach was evaluated using three performance measures namely sensitivity, specificity and classification accuracy. Further, the proposed approach was compared with the existing state‐of‐the‐art techniques in terms of three performance measures, ROC curves, scoring and computation time. Irrespective of the datasets and the classifiers, the proposed method outperformed the existing methods. In addition, the statistical test also demonstrated that the proposed method is significantly better in comparison to the other existing methods. The appreciable performance of the proposed method supports that it will assist clinicians/researchers in the classification of AD patients and controls.     

Alzheimer disease classification using KPCA,LDA, and multi‐kernel learning SVM

Early diagnosis of Alzheimer disease (AD) and mild cognitive impairment (MCI) is always useful. Preventive measures might have an impact on reducing AD risk factors. Structural magnetic resonance (MR) imaging, one of the vital sensitive biomarkers for cerebral atrophy in the brain, is used to extract volumetric feature by FreeSurfer and the CIVET toolbox. All of the structural magnetic resonance imaging (s‐MRI) data that we used were downloaded from the Alzheimer's disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu) of imaging data. This novel approach is applied for the diagnosis of AD and MCI from healthy controls (HCs) combining extracted features with the MMSE (mini‐mental state examination) scores, applying a two sample t‐test to select a subset of features. The subset of features is fed to kernel principal component analysis (KPCA) module to project data onto the reduced principal component coefficients at higher dimensional kernel space to increase the linear separability. Then, the kernel PCA coefficients are projected into the more efficient linear discriminant space using linear discriminant analysis. A multi‐kernel learning support vector machine (SVM) is used on newly projected data for stratification of AD and MCI from HCs. Using this approach, we obtain 93.85% classification accuracy when detecting AD from HCs for segmented volumetric features (using FreeSurfer) with high sensitivity and specificity. When distinguishing MCI from HCs and AD using volumetric features after subcortical segmentation, the detection rate reaches 86.54% and 75.12%, respectively.