Framework for COVID-19 Segmentation and Classification Based on Deep Learning of Computed Tomography Lung Images

Corona Virus Disease 2019 (COVID-19) has affected millions of people worldwide and caused more than 6.3 million deaths (World Health Organization, June 2022). Increased attempts have been made to develop deep learning methods to diagnose COVID-19 based on computed tomography (CT) lung images. It is a challenge to reproduce and obtain the CT lung data, because it is not publicly available. This paper introduces a new generalized framework to segment and classify CT images and determine whether a patient is tested positive or negative for COVID-19 based on lung CT images. In this work, many different strategies are explored for the classification task. ResNet50 and VGG16 models are applied to classify CT lung images into COVID-19 positive or negative. Also, VGG16 and ReNet50 combined with U-Net, which is one of the most used architectures in deep learning for image segmentation, are employed to segment CT lung images before the classifying process to increase system performance. Moreover, the image size dependent normalization technique (ISDNT) and Wiener filter are utilized as the preprocessing techniques to enhance images and noise suppression. Additionally, transfer learning and data augmentation techniques are performed to solve the problem of COVID-19 CT lung images deficiency, therefore the over-fitting of deep models can be avoided. The proposed frameworks, which comprised of end-to-end, VGG16, ResNet50, and U-Net with VGG16 or ResNet50, are applied on the dataset that is sourced from COVID-19 lung CT images in Kaggle. The classification results show that using the preprocessed CT lung images as the input for U-Net hybrid with ResNet50 achieves the best performance. The proposed classification model achieves the 98.98% accuracy (ACC), 98.87% area under the ROC curve (AUC), 98.89% sensitivity (Se), 97.99 % precision (Pr), 97.88% F₁-score, and 1.8974-seconds computational time.     

基于Gabor和灰度共生矩阵混合特征叶片泵装配质量检测

本文提出一种Gabor和灰度共生矩阵相结合的特征来检测叶片泵中叶片装配质量的方法。首先构建叶片图像数据集,用5种尺度的和4种方向的Gabor滤波器对图像滤波,根据滤波后的图像计算得到幅值特征图,然后提取幅值特征图的灰度共生矩阵特征,最后融合归一化各个幅值特征图提取到的特征,利用主成分分析法降维,并用这些特征向量训练支持向量机(SVM)分类器,实现对叶片装配质量的评估。将本文提出的混合特征与LBP特征、灰度共生矩阵分别进行了比较得到的分类效果约提高了约10%。基于Gabor和灰度共生矩阵混合特征的叶片装配质量检测准确率提升到了93%。实验结果表明Gabor特征和灰度共生矩阵结合后能够很好从多尺度、多方向上提取图像的纹理特征,并应用于图像分类取得了良好的效果,在一些图像识别上有很宽广的应用前景。     

Network traffic classification method basing on CNN

Since the feature selection process will directly affect the accuracy of the traffic classification based on the traditional machine learning method,a traffic classification algorithm based on convolution neural network was tailored.First,the min-max normalization method was utilized to process the traffic data and map them into gray images,which would be used as the input data of convolution neural network to realize the independent feature learning.Then,an improved structure of the classical convolution neural network was proposed,and the parameters of the feature map and the full connection layer were designed to select the optimal classification model to realize the traffic classification.The tailored method can improve the classification accuracy without the complex operation of the network traffic.A series of simulation test results with the public data sets and real data sets show that compared with the traditional classification methods,the tailored convolution neural network traffic classification method can improve the accuracy and reduce the time of classification.     

Maximally Informative Feature and Sensor Selection in Pattern Recognition Using Local and Global Independent Component Analysis

In pattern recognition, a suitable criterion for feature selection is the mutual information (MI) between feature vectors and class labels. Estimating MI in high dimensional feature spaces is problematic in terms of computation load and accuracy. We propose an independent component analysis based MI estimation (ICA-MI) methodology for feature selection. This simplifies the high dimensional MI estimation problem into multiple one-dimensional MI estimation problems. Nonlinear ICA transformation is achieved using piecewise local linear approximation on partitions in the feature space, which allows the exploitation of the additivity property of entropy and the simplicity of linear ICA algorithms. Number of partitions controls the tradeoff between more accurate approximation of the nonlinear data topology and small-sample statistical variations in estimation. We test the ICA-MI feature selection framework on synthetic, UCI repository, and EEG activity classification problems. Experiments demonstrate, as expected, that the selection of the number of partitions for local linear ICA is highly problem dependent and must be carried out properly through cross validation. When this is done properly, the proposed ICA-MI feature selection framework yields feature ranking results that are comparable to the optimal probability of error based feature ranking and selection strategy at a much lower computational load.     

Texture-based classification of atherosclerotic carotid plaques

There are indications that the morphology of atherosclerotic carotid plaques, obtained by high-resolution ultrasound imaging, has prognostic implications. The objective of this study was to develop a computer-aided system that will facilitate the characterization of carotid plaques for the identification of individuals with asymptomatic carotid stenosis at risk of stroke. A total of 230 plaque images were collected which were classified into two types: symptomatic because of ipsilateral hemispheric symptoms, or asymptomatic because they were not connected with ipsilateral hemispheric events. Ten different texture feature sets were extracted from the manually segmented plaque images using the following algorithms: first-order statistics, spatial gray level dependence matrices, gray level difference statistics, neighborhood gray tone difference matrix, statistical feature matrix, Laws texture energy measures, fractal dimension texture analysis, Fourier power spectrum and shape parameters. For the classification task a modular neural network composed of self-organizing map (SOM) classifiers, and combining techniques based on a confidence measure were used. Combining the classification results of the ten SOM classifiers inputted with the ten feature sets improved the classification rate of the individual classifiers, reaching an average diagnostic yield (DY) of 73.1%. The same modular system was implemented using the statistical k-nearest neighbor (KNN) classifier. The combined DY for the KNN system was 68.8%. The results of this paper show that it is possible to identify a group of patients at risk of stroke based on texture features extracted from ultrasound images of carotid plaques. This group of patients may benefit from a carotid endarterectomy whereas other patients may be spared from an unnecessary operation.     

Fake news and COVID-19: modelling the predictors of fake news sharing among social media users

Fake news dissemination on COVID-19 has increased in recent months, and the factors that lead to the sharing of this misinformation is less well studied. Therefore, this paper describes the result of a Nigerian sample (n = 385) regarding the proliferation of fake news on COVID-19. The fake news phenomenon was studied using the Uses and Gratification framework, which was extended by an “altruism” motivation. The data were analysed with Partial Least Squares (PLS) to determine the effects of six variables on the outcome of fake news sharing. Our results showed that altruism was the most significant factor that predicted fake news sharing of COVID-19. We also found that social media users’ motivations for information sharing, socialisation, information seeking and pass time predicted the sharing of false information about COVID-19. In contrast, no significant association was found for entertainment motivation. We concluded with some theoretical and practical implications.     

Sec-Eliminating the SARS-CoV-2 by AlGaN Based High Power Deep Ultraviolet Light Source

The world-wide spreading of coronavirus disease (COVID-19) has greatly shaken human society, thus effective and fast-speed methods of non-daily-life-disturbance sterilization have become extremely significant. In this work, by fully benefitting from high-quality AlN template (with threading dislocation density as low as ≈6×10⁸ cm⁻²) as well as outstanding deep ultraviolet (UVC-less than 280 nm) light-emitting diodes (LEDs) structure design and epitaxy optimization, high power UVC LEDs and ultra-high-power sterilization irradiation source are achieved. Moreover, for the first time, a result in which a fast and complete elimination of SARS-CoV-2 (the virus causes COVID-19) within only 1 s is achieved by the nearly whole industry-chain-covered product. These results advance the promising potential in UVC-LED disinfection particularly in the shadow of COVID-19.     

采用音质特征和VLAD编码的新冠肺炎检测算法

2020年,世界卫生组织宣布COVID-19疫情为大流行病。为了实现COVID-19快速地、可靠地检测,本研究通过语音信号分析技术来寻找感染COVID-19的语音信号特征,利用咳嗽声片段和语音片段对是否感染COVID-19做出自动判断。在INTERSPEECH 2021 ComParE竞赛提供的相关数据集和baseline的基础上,本文首先利用语音端点检测技术对数据集进行增广,其次在特征集中加入语音质量特征,使相关baseline结果得到了提升,证明了语音质量特征在对COVID-19自动语音检测任务上的有效性。同时,引入局部聚合描述子向量对低级别特征进行编码,当字典大小较小时,有效地提升了系统的分类性能。最后,对多种算法得到的分类结果进行融合,进一步提升分类效果,最终在两个子任务中的验证集上UAR分别取得了73.9%和77.2%。      

CSV: Image quality assessment based on color,structure, and visual system

This paper presents a full-reference image quality estimator based on color, structure, and visual system characteristics denoted as CSV. In contrast to the majority of existing methods, we quantify perceptual color degradations rather than absolute pixel-wise changes. We use the CIEDE2000, color difference formulation to quantify low-level color degradations and the Earth Mover's Distance between color name probability vectors to measure significant color degradations. In addition to the perceptual color difference, CSV also contains structural and perceptual differences. Structural feature maps are obtained by mean subtraction and divisive normalization, and perceptual feature maps are obtained from contrast sensitivity formulations of retinal ganglion cells. The proposed quality estimator CSV is tested on the LIVE, the Multiply Distorted LIVE, and the TID 2013 databases, and it is always among the top two performing quality estimators in terms of at least ranking, monotonic behavior or linearity.     

The gray level aura matrices for textured image segmentation

Inspired by an intuitive analogy that exists between the gray level textures and the miscibility in the multiphase fluids, the aura concept was developed from set theory tools in order to modeling the texture image. The gray level aura matrix (GLAM) has been then proposed to generalize the gray level cooccurrence matrix (GLCM) which remains very popular in the texture analysis. The GLAM indicates how much each gray level is present in the neighborhood of each other gray level. The neighborhood is defined by a structuring element as one used in mathematical morphology. The GLAM is mainly used and studied in synthesis and classification of textures framework but very few works are devoted to the segmentation. The aim of this paper is to exploit the GLAM for the segmentation of textured images. Experiments results over synthetic and real images show the efficiency of the GLAM. The influence of the shape and the size of the structuring element on the segmentation results are also studied.     

基于ROC曲线的目标识别性能评估方法

在自动目标识别（ATR）领域，评估目标识别算法性能的指标常用的有分类准确度、精确度、检测概率、混淆矩阵等，但这些指标都存在固有的局限性，对类别先验概率不具有稳健性。近几年来采用的基于雷达接收机工作特性曲线即ROC曲线的评估方法得到的评估结论不敏感于类别先验概率，从根本上克服了以上指标的缺陷。同时，该评估方法可以在错误分类代价未知的情况下进行，并能对识别算法进行多门限评估，因而在分类器识别算法性能评估中得到了广泛应用。文中首先叙述了ROC曲线和ROC曲线建立的方法，然后详细论述了基于ROC曲线的评估方法中常用的性能评估指标。     

A novel gray image representation using overlapping rectangular NAM and extended shading approach

In this paper, inspired by the idea of overlapping rectangular region coding of binary images, we extend the SDS design, which is based on overlapping representation from binary images to gray images based on the non-symmetry and anti-packing model (NAM). A novel gray image representation is proposed by using the overlapping rectangular NAM (RNAM) and the extended Gouraud shading approach, which is called ORNAM representation. Also, we present an ORNAM representation algorithm of gray images. The encoding and the decoding of the proposed algorithm can be performed in O(n log n) time and O(n) time, respectively, where n denotes the number of pixels in a gray image. The wrong decoding problem of the hybrid matrix R for the overlapping RNAM representation of gray images is solved by using the horizontal, vertical, and isolated matrices, i.e., H, V and I, respectively, which are used to identify the vertex types. Also, we put forward four criteria of anti-packing homogeneous blocks. In addition, by redefining a codeword set for the three vertices symbols, we also propose a new coordinate data compression procedure for coding the coordinates of all non-zone elements in the three matrices H, V and I. By taking some idiomatic standard gray images in the field of image processing as typical test objects, and by comparing our proposed ORNAM representation with the conventional S-Tree Coding (STC) representation, the experimental results in this paper show that the former has higher compression ratio and less number of homogeneous blocks than the latter whereas maintaining a satisfactory image quality, and therefore it is a better method to represent gray images.     

Generating segmented meshes from textured color images

This paper presents a new framework for generating triangular meshes from textured color images. The proposed framework combines a texture classification technique, called W-operator, with Imesh, a method originally conceived to generate simplicial meshes from gray scale images. An extension of W-operators to handle textured color images is proposed, which employs a combination of RGB and HSV channels and Sequential Floating Forward Search guided by mean conditional entropy criterion to extract features from the training data. The W-operator is built into the local error estimation used by Imesh to choose the mesh vertices. Furthermore, the W-operator also enables to assign a label to the triangles during the mesh construction, thus allowing to obtain a segmented mesh at the end of the process. The presented results show that the combination of W-operators with Imesh gives rise to a texture classification-based triangle mesh generation framework that outperforms pixel based methods.     

A HiPAD Integrated with rGO/MWCNTs Nano-Circuit Heater for Visual Point-of-Care Testing of SARS-CoV-2

Nowadays, the main obstacle for further miniaturization and integration of nucleic acids point-of-care testing devices is the lack of low-cost and high-performance heating materials for supporting reliable nucleic acids amplification. Herein, reduced graphene oxide hybridized multi-walled carbon nanotubes nano-circuit integrated into an ingenious paper-based heater is developed, which is integrated into a paper-based analytical device (named HiPAD). The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still raging across the world. As a proof of concept, the HiPAD is utilized to visually detect the SARS-CoV-2 N gene using colored loop-mediated isothermal amplification reaction. This HiPAD costing a few dollars has comparable detection performance to traditional nucleic acids amplifier costing thousands of dollars. The detection range is from 25 to 2.5 × 10¹⁰ copies mL⁻¹ in 45 min. The detection limit of 25 copies mL⁻¹ is 40 times more sensitive than 1000 copies mL⁻¹ in conventional real-time PCR instruments. The disposable paper-based chip could also avoid potential secondary transmission of COVID-19 by convenient incineration to guarantee biosafety. The HiPAD or easily expanded M-HiPAD (for multiplex detection) has great potential for pathogen diagnostics in resource-limited settings.     

基于SVM混合核的遥感图像变化检测

针对由实际遥感地物类型难以确定导致的多光谱遥感影像变化检测精度较低的问题,提出一种基于SVM混合核的遥感图像变化检测。首先利用CVA算法构造差异影像,其次利用灰度共生矩阵提取差异影像的纹理特征与差异影像的灰度特征组成特征向量,接着利用差异影像的直方图选择置信度高的训练样本,并利用构造的SVM混合核进行训练得到分类超平面,最后利用SVM混合核函数对差异影像进行二分类得到最后的变化检测结果。实际遥感数据验证结果表明,所构造的SVM混合核函数用于多光谱遥感影像变化检测中是可行、有效的。     

Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization.

Mass segmentation is used as the first step in many computer-aided diagnosis (CAD) systems for classification of breast masses as malignant or benign. The goal of this paper was to study the accuracy of an automated mass segmentation method developed in our laboratory, and to investigate the effect of the segmentation stage on the overall classification accuracy. The automated segmentation method was quantitatively compared with manual segmentation by two expert radiologists (R1 and R2) using three similarity or distance measures on a data set of 100 masses. The area overlap measures between R1 and R2, the computer and R1, and the computer and R2 were 0.76 +/- 0.13, 0.74 +/- 0.11, and 0.74 +/- 0.13, respectively. The interobserver difference in these measures between the two radiologists was compared with the corresponding differences between the computer and the radiologists. Using three similarity measures and data from two radiologists, a total of six statistical tests were performed. The difference between the computer and the radiologist segmentation was significantly larger than the interobserver variability in only one test. Two sets of texture, morphological, and spiculation features, one based on the computer segmentation, and the other based on radiologist segmentation, were extracted from a data set of 249 films from 102 patients. A classifier based on stepwise feature selection and linear discriminant analysis was trained and tested using the two feature sets. The leave-one-case-out method was used for data sampling. For case-based classification, the area Az under the receiver operating characteristic (ROC) curve was 0.89 and 0.88 for the feature sets based on the radiologist segmentation and computer segmentation, respectively. The difference between the two ROC curves was not statistically significant.     

A semi-automated system based on level sets and invariant spatial interrelation shape features for Caenorhabditis elegans phenotypes

Caenorhabditis elegans shares several molecular and physiological homologies with humans and thus plays a key role in studying biological processes. As a consequence, much progress has been made in automating the analysis of C. elegans. However, there is still a strong need to achieve more progress in automating the analysis of static images of adult worms. In this paper, a three-phase semi-automated system has been proposed. As a first phase, a novel segmentation framework, based on variational level sets and local pressure force function, has been introduced to handle effectively images corrupted with intensity inhomogeneity. Then, a set of robust invariant symbolic features for high-throughput screening of image-based C. elegans phenotypes are extracted. Finally, a classification model is applied to discriminate between the different subsets. The proposed system demonstrates its effectiveness in measuring morphological phenotypes in individual worms of C. elegans.