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.     

Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

Magnetic resonance imaging (MRI) is increasingly used in the diagnosis of Alzheimer's disease (AD) in order to identify abnormalities in the brain. Indeed, cortical atrophy, a powerful biomarker for AD, can be detected using structural MRI (sMRI), but it cannot detect impairment in the integrity of the white matter (WM) preceding cortical atrophy. The early detection of these changes is made possible by the novel MRI modality known as diffusion tensor imaging (DTI). In this study, we integrate DTI and sMRI as complementary imaging modalities for the early detection of AD in order to create an effective computer-assisted diagnosis tool. The fused Bag-of-Features (BoF) with Speeded-Up Robust Features (SURF) and modified AlexNet convolutional neural network (CNN) are utilized to extract local and deep features. This is applied to DTI scalar metrics (fractional anisotropy and diffusivity metric) and segmented gray matter images from T1-weighted MRI images. Then, the classification of local unimodal and deep multimodal features is first performed using support vector machine (SVM) classifiers. Then, the majority voting technique is adopted to predict the final decision from the ensemble SVMs. The study is directed toward the classification of AD versus mild cognitive impairment (MCI) versus cognitively normal (CN) subjects. Our proposed method achieved an accuracy of 98.42% and demonstrated the robustness of multimodality imaging fusion.     

Algorithm for training the minimum error one-class classifier of images

We propose a training algorithm for one-class classifiers in order to minimize the classification error. The aim is to choose the optimal value of the slack parameter, which controls the selectiveness of a classifier. The one-class classifier based on the coordinated clusters representation of images is trained and then used for the classification of texture images. As the slack parameter C varies through a range of values, for each C, the misclassification rate is computed using only the training samples. The value of C that yields the minimum misclassification rate, estimated over the training set, is taken as the optimal value, C(opt). Finally, the optimized classifier is tested on the extended database of images. Experimental results demonstrate the validity of the proposed method. In our experiments, classification efficiency approaches, or is equal to, 100%, after the optimal training of the classifier.     

A multi‐modal,multi‐atlas‐based approach for Alzheimer detection via machine learning

The use of biomarkers for early detection of Alzheimer's disease (AD) improves the accuracy of imaging‐based prediction of AD and its prodromal stage that is mild cognitive impairment (MCI). Brain parcellation‐based computer‐aided methods for detecting AD and MCI segregate the brain in different anatomical regions and use their features to predict AD and MCI. Brain parcellation generally is carried out based on existing anatomical atlas templates, which vary in the boundaries and number of anatomical regions. This works considers dividing the brain based on different atlases and combining the features extracted from these anatomical parcellations for a more holistic and robust representation. We collected data from the ADNI database and divided brains based on two well‐known atlases: LONI Probabilistic Brain Atlas (LPBA40) and Automated Anatomical Labeling (AAL). We used baselines images of structural magnetic resonance imaging (MRI) and ¹⁸F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) to calculate average gray‐matter density and average relative cerebral metabolic rate for glucose in each region. Later, we classified AD, MCI and cognitively normal (CN) subjects using the individual features extracted from each atlas template and the combined features of both atlases. We reduced the dimensionality of individual and combined features using principal component analysis, and used support vector machines for classification. We also ranked features mostly involved in classification to determine the importance of brain regions for accurately classifying the subjects. Results demonstrated that features calculated from multiple atlases lead to improved performance compared to those extracted from one atlas only.     

Identifying brain regions contributing to Alzheimer's disease using self regulating particle swarm optimization

In this article, we developed an approach for detecting brain regions that contribute to Alzheimer's disease (AD) using support vector machine (SVM) classifiers and the recently developed self regulating particle swarm optimization (SRPSO) algorithm. SRPSO employs strategies inspired by the principles of learning in humans to achieve faster and better optimization results. The classifiers for distinguishing subjects into AD patients and cognitively normal (CN) individuals were built using grey matter (GM) and white matter (WM) volumetric features extracted from structural magnetic resonance (MR) images. It could be observed from results that the classifier built using both GM and WM features provided accuracy of 89.26% which is better than the performance of classifiers built using either GM or WM features only. Moreover, consideration of clinical features in addition to volumetric features improves the accuracy further to 94.63% which is better than the performance reported by recent works in literature. In order to identify the brain regions that are important for AD vs CN classification problem, we used SRPSO to extract GM and WM features that yield better classification performance. Using 50 features identified by SRPSO, an accuracy of 89.39% was obtained which is close to the accuracy based on all features. The features identified by SRPSO were mapped back to the brain to identify brain regions that exhibit degeneration in AD. In addition to identifying areas known to be involved in AD like cerebellum, hippocampus, this helped in finding newer areas that might contribute towards AD.     

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.     

基于贝叶斯最优核判别分析的机械故障诊断

针对在该方法中,用核判别分析到机械故障诊断时核参数选取困难的问题,提出了一种基于贝叶斯最优核判别分析的机械故障诊断方法。首先采用梯度下降法优化同方差性准则确定最优核参数;然后采用最优核参数使用核判别分析将原始样本投影到一个最优子空间,在该子空间中各类样本具有最佳判别性;最后基于投影后的样本使用最近邻方法进行故障分类。将该方法应用于滚动轴承故障诊断,并与相关方法的诊断结果进行了比较,实验结果表明:该方法可获得与支持向量机同样的诊断正确率,并实现了最优核参数的自动选择。     

Dual‐Modal NIR‐Fluorophore Conjugated Magnetic Nanoparticle for Imaging Amyloid‐β Species In Vivo

Senile plaques, the extracellular deposit of amyloid‐β (Aβ) peptides, are one of the neuropathological hallmarks found in Alzheimer's disease (AD) brain. The current method of brain imaging of amyloid plaques based on positron emission tomography (PET) is expensive and invasive with low spatial resolution. Thus, the development of sensitive and nonradiative amyloid‐β (Aβ)‐specific contrast agents is highly important and beneficial to achieve early AD detection, monitor the disease progression, and evaluate the effectiveness of potential AD drugs. Here a neuroprotective dual‐modal nanoprobe developed by integrating highly Aβ‐specific and turn‐on fluorescence cyanine sensors with superparamagnetic iron oxide nanoparticles as an effective near‐infrared imaging (NIRI)/magnetic resonance imaging (MRI) contrast agent for imaging of Aβ species in vivo is reported. This Aβ‐specific probe is found not only nontoxic and noninvasive, but also highly blood brain barrier permeable. It also shows a potent neuroprotective effect against Aβ‐induced toxicities. This nanoprobe is successfully applied for in vivo fluorescence imaging with high sensitivity and selectivity to Aβ species, and MRI with high spatial resolution in an APP/PS1 transgenic mice model. Its potential as a powerful in vivo dual‐modal imaging tool for early detection and diagnosis of AD in humans is affirmed.     

Computational intelligence techniques for human brain MRI classification

This study proposes an image classification methodology that automatically classifies human brain magnetic resonance (MR) images. The proposed methods contain four main stages: Data acquisition, preprocessing, feature extraction, feature reduction and classification, followed by evaluation. First stage starts by collecting MRI images from Harvard and our constructed Egyptian database. Second stage starts with noise reduction in MR images. Third stage obtains the features related to MRI images, using stationary wavelet transformation. In the fourth stage, the features of MR images have been reduced using principles of component analysis and kernel linear discriminator analysis (KLDA) to the more essential features. In last stage, the classification stage, two classifiers have been developed to classify subjects as normal or abnormal MRI human images. The first classifier is based on K‐Nearest Neighbor (KNN) on Euclidean distance. The second classifier is based on Levenberg‐Marquardt (LM‐ANN). Classification accuracy of 100% for KNN and LM‐ANN classifiers has been obtained. The result shows that the proposed methodologies are robust and effective compared with other recent works.     

FHDT: Fuzzy and Hyco-entropy-based Decision Tree Classifier for Tuberculosis Diagnosis from Sputum Images

Tuberculosis (TB) is one of the infectious diseases spread by the infectious agent Mycobacterium tuberculosis. Sputum smear microscopy is the primary tool used for the diagnosis of pulmonary TB, but has its limitations such as low sensitivity and large observation time. Hence, an automated technique is preferred for the diagnosis of TB. This paper develops a technique for TB diagnosis based on the bacilli count by proposing Fuzzy and Hyco-entropy-based Decision Tree (FHDT) classifier using sputum smear microscopic images. The proposed technique involves three steps: segmentation, feature extraction and classification. Initially, the input sputum smear microscopic image is subjected to a colour space transformation, for which a thresholding is applied to obtain the segmented result. Important features such as length, density, area and few histogram features are extracted for FHDT-based classification that classifies the segments into few-bacilli, non-bacilli and overlapping bacilli. An entropy function, called hyco-entropy, is designed for the optimal selection of feature. For further analysis of classification, that is, to count the number in the overlapping bacilli, the fuzzy classifier is adopted. FHDT classifier is evaluated in terms of Segmentation Accuracy (SA), Mean Squared Error (MSE) and Missing Count (MC) using microscopic images taken from ZNSM-iDB, where it can attain maximum mean SA of 0.954 and mean MC of 2.4.     

Comparative analysis of Alzheimer's disease classification by CDR level using CNN,feature selection,and machine-learning techniques

Magnetic resonance imaging (MRI) of brain needs an impeccable analysis to investigate all its structure and pattern. This analysis may be a sharp visual analysis by an experienced medical professional or by a computer aided diagnosis system that can help to predict, what may be the recent condition. Similarly, on the basis of various information and technique, a system can be designed to detect whether a patient is prone to Alzheimer's disease or not. And this task of detection of abnormalities at an initial stage from brain MRI is a major challenge in the field of neurosciences. The main idea behind our research is to utilize the deep layers feature extraction benefited from deep neural network architecture, without extensive hardware resource training, and classifying the image on a basis of simple machine-learning algorithm with selected best features in order to reduce work load, classification error and hardware utilization time. We have utilized convolution neural network (CNN) layer using similar architecture like that of Alexnet with some parametric change, for the automatic extraction of features of images obtained from slice extraction of whole brain MRI whereas 13 manual features based on gray level co-occurrence matrix were also extracted to test the impact of this features on ranking. If we had only classified using CNN network, the misclassification rate was much higher. So, feature selection is achieved with feature ranking algorithms like Mutinffs, ReliefF, Laplacian and UDFS and so on and also tested with different machine-learning techniques like Support Vector Machine, K-Nearest Neighbor and Subspace Ensemble under different testing condition. The performance of the result is satisfactory with classification accuracy around 98% to 99% with 7:3 ratio of random holdout partition of training to testing image sets and also with fivefolds of cross-validation on the same set using a standardized template.     

Glioma brain tumor detection and segmentation using weighting random forest classifier with optimized ant colony features

The uncontrolled growth of cells in brain regions leads to the tumor regions and these abnormal tumor regions are scanned by magnetic resonance imaging (MRI) technique as an image. This paper proposes random forest classifier based Glioma brain tumor detection and segmentation methodology using feature optimization technique. The texture features are derived from brain MRI image and these derived feature set are now optimized by ant colony optimization algorithm. These optimized set of features are trained and classified using random forest classification method. This classifier classifies the brain MRI image into Glioma or non-Glioma image based on the optimized set of features. Furthermore, energy-based segmentation method is applied on the classified Glioma image for segmenting the tumor regions. The proposed methodology for Glioma brain tumor stated in this paper achieves 97.7% of sensitivity, 96.5% of specificity, and 98.01% of accuracy.     

Neuroimaging computer-aided diagnosis systems for Alzheimer's disease

This paper has reviewed the state-of-the-art approaches for Computer Aided Diagnosis Systems (CADS) for Alzheimer's Disease (AD) using neuroimaging. Identification of the current approaches leads to improving the efficiency of these techniques. The analysis covered 110 articles published between 2009 and January 2018. Papers were chosen according to the Newcastle-Ottawa criteria. MeSH terms were “computer aided diagnosis systems for Alzheimer's disease” and “computer aided diagnosis systems methods for diagnosis of Alzheimer's disease”. CADS algorithms have been presented with specific methods. There is no standardized approach to determine the best one. This study has tables that aimed to conclude all methods in a precise way. Among them, Statistical Parametric Mapping (SPM), Principal Component Analysis (PCA), and Support Vector Machine (SVM) were the most common, respectively. CADS for AD could become important in clinical practice in the near future. The evaluation criteria approved their efficiency as a second opinion besides the neurologist.     

Antibody‐Mimetic Peptoid Nanosheet for Label‐Free Serum‐Based Diagnosis of Alzheimer's Disease

Alzheimer's disease (AD) is the most common form of dementia characterized by progressive cognitive decline. Current diagnosis of AD is based on symptoms, neuropsychological tests, and neuroimaging, and is usually evident years after the pathological process. Early assessment at the preclinical or prodromal stage is in a great demand since treatment after the onset can hardly stop or reverse the disease progress. However, early diagnosis of AD is challenging due to the lack of reliable noninvasive approaches. Here, an antibody‐mimetic self‐assembling peptoid nanosheet containing surface‐exposed Aβ42‐recognizing loops is constructed, and a label‐free sensor for the detection of AD serum is developed. The loop‐displaying peptoid nanosheet is demonstrated to have high affinity to serum Aβ42, and to be able to identify AD sera with high sensitivity. The dense distribution of molecular recognition loops on the robust peptoid nanosheet scaffold not only mimics the architecture of antibodies, but also reduces the nonspecific binding in detecting multicomponent samples. This antibody‐mimetic 2D material holds great potential toward the blood‐based diagnosis of AD, and meanwhile provides novel insights into the antibody alternative engineering and the universal application in biological and chemical sensors.     

基于可视图图谱幅值熵的滚动轴承故障诊断方法

为了准确有效地提取滚动轴承振动信号的非平稳、非线性故障特征,将复杂网络与图信号处理技术(graph signal processing,GSP)引入机械故障诊断领域,提出了基于可视图图谱幅值熵(graph spectrum amplitude entropy of visibility graph,GSAE VG)的滚动轴承故障诊断方法。该方法先将滚动轴承振动信号转换为可视图,获得可视图信号;再通过图傅里叶变换(graph Fourier transform,GFT)将可视图信号从顶点域变换到图谱域,并将计算得到的图谱幅值熵(graph spectrum amplitude entropy,GSAE)作为故障特征参数;利用马氏距离(Mahalanobis distance,MD)判别函数作为分类器对不同类型故障进行模式识别。实际滚动轴承振动信号的分析结果表明,基于可视图图谱幅值熵的故障诊断方法能对滚动轴承故障进行准确有效地识别。     

基于进化蒙特卡洛方法的特征选择在机械故障诊断中的应用

特征选择可以从原始特征集中去除冗余特征,选择出优化特征子集,提高机械故障诊断精度和诊断效率。将进化蒙特卡洛方法引入机械故障诊断的特征选择。应用支持向量机(SVM)作为故障决策器,采用Wrapper式特征子集评价标准,并采用进化蒙特卡洛算法搜索最优特征子集。运用滚动轴承故障振动信号数据对提出的方法进行验证,实验结果表明该方法是有效的。     

Traffic Sign Recognition Method Integrating Multi-Layer Features and Kernel Extreme Learning Machine Classifier

Traffic sign recognition (TSR), as a critical task to automated driving and driver assistance systems, is challenging due to the color fading, motion blur, and occlusion. Traditional methods based on convolutional neural network (CNN) only use an end-layer feature as the input to TSR that requires massive data for network training. The computation-intensive network training process results in an inaccurate or delayed classification. Thereby, the current state-of-the-art methods have limited applications. This paper proposes a new TSR method integrating multi-layer feature and kernel extreme learning machine (ELM) classifier. The proposed method applies CNN to extract the multi-layer features of traffic signs, which can present sufficient details and semantically abstract information of multi-layer feature maps. The extraction of multi-scale features of traffic signs is effective against object scale variation by applying a new multi-scale pooling operation. Further, the extracted features are combined into a multi-scale multi-attribute vector, which can enhance the feature presentation ability for TSR. To efficiently handle nonlinear sampling problems in TSR, the kernel ELM classifier is adopted for efficient TSR. The kernel ELM has a more powerful function approximation capability, which can achieve an optimal and generalized solution for multiclass TSR. Experimental results demonstrate that the proposed method can improve the recognition accuracy, efficiency, and adaptivity to complex travel environments in TSR.     

A Novel DDoS Attack Detection Method Using Optimized Generalized Multiple Kernel Learning

Distributed Denial of Service (DDoS) attack has become one of the most destructive network attacks which can pose a mortal threat to Internet security. Existing detection methods cannot effectively detect early attacks. In this paper, we propose a detection method of DDoS attacks based on generalized multiple kernel learning (GMKL) combining with the constructed parameter R. The super-fusion feature value (SFV) and comprehensive degree of feature (CDF) are defined to describe the characteristic of attack flow and normal flow. A method for calculating R based on SFV and CDF is proposed to select the combination of kernel function and regularization paradigm. A DDoS attack detection classifier is generated by using the trained GMKL model with R parameter. The experimental results show that kernel function and regularization parameter selection method based on R parameter reduce the randomness of parameter selection and the error of model detection, and the proposed method can effectively detect DDoS attacks in complex environments with higher detection rate and lower error rate.     

Enhanced diagnostics using orthogonal de-noising based nonlinear discriminant analysis and its application to multivariate data

Many multivariate statistical techniques have been developed to solve fault diagnosis problems for productivity and quality improvement. Recently, nonlinear kernel techniques (e.g. support vector machines) have been successfully applied to a number of applications such as bio-informatics, face recognition, handwritten digit recognition, etc. The basis of these techniques is to map input data into a nonlinear space; these mapped data are then analysed. Using the kernel trick on these methods makes it possible to develop powerful kernel-based nonlinear techniques and to extract information from the mapped data. This paper proposes a new diagnosis method based on kernel Fisher discriminant analysis (KFDA), a nonlinear kernel technique. It utilizes KFDA to extract nonlinear patterns of data effectively. In addition, an orthogonal de-noising technique, called orthogonal signal correction, is incorporated into the proposed framework and used as a pre-processing step. This de-noising is executed before KFDA modeling in order to remove unwanted variation of data. A case study on two processes has been conducted. The proposed method produced reliable diagnosis results and the use of KFDA modeling combined with the orthogonal de-noising technique was able to improve the classification performance.     

层次型支持向量机人脸检测器

大量的数值实验表明，在非线性支持向量机中，核函数的选取对支持向量机性能的影响很大。核函数的选择一直是一个难题。本文利用遗传算法的全局优化能力比较并分析了多项式核函数和高斯核函数的检测正确率和支持向量个数两项指标，提出了一种层次型支持向量机人脸检测器。实验结果证明，本文的方法确实取得了较好的效果。