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A survey of machine learning techniques for detecting and diagnosing COVID-19 from imaging

Quantitative Biology 2022年第2期10卷 188-207页

作者： Aishwarza Panday Muhammad Ashad Kabir Nihad Karim Chowdhury Department of Computer Science&Engineering Stamford UniversityDhaka 1217Bangladesh School of Computing and Mathematics Charles Sturt UniversityNSW 2795Australia Department of Computer Science&Engineering University of ChittagongChittagong 4349Bangladesh

Background: Due to the limited availability and high cost of the reverse transcription-polymerase chain reaction (RT- PCR) test, many studies have proposed machine learning techniques for detecting COVID-19 from medical imaging. The purpose of this study is to systematically review, assess and synthesize research articles that have used different machine learning techniques to detect and diagnose COVID-19 from chest X-ray and CT scan images.Methods: A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey solely centered on reproducible and high-quality research. We selected papers based on our inclusion criteria.Results: In this survey, we reviewed 98 articles that fulfilled our inclusion criteria. We have surveyed a complete pipeline of chest imaging analysis techniques related to COVID-19, including data collection, pre-processing, feature extraction, classification, and visualization. We have considered CT scans and X-rays as both are widely used to describe the latest developments in medical imaging to detect COVID-19.Conclusions: This survey provides researchers with valuable insights into different machine learning techniques and their performance in the detection and diagnosis of COVID-19 from chest imaging. At the end, the challenges and limitations in detecting COVID-19 using machine learning techniques and the future direction of research are discussed.

关键词： COVID-19 machine learning deep learning detection classification diagnosing X-ray CT scan

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diagnosing Kearns-Sayre Syndrome Requires Genetic Confirmation

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Chinese Medical Journal 2016年第18期129卷 2267-2268页

作者： Josef Finsterer Sinda Zarrouk-Mahjoub 1Krankenanstalt Rudolfstiftung 1030 Vienna Austria Genomics Platform Pasteur Institute of Tunis 1002 Tunis Tunisia

To the Editor： With interest, we read the article by Kwon et al. about a single patient with Kearns-Sayre syndrome （KSS）, who successfully and without complications underwent peripheral nerve block for multiple mus... 详细信息

关键词： diagnosing Kearns-Sayre Syndrome Requires

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diagnosing Dysfunctional Coping in HIV-Infected Persons

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Open Journal of Medical Psychology 2019年第2期8卷 36-43页

作者： O. H. Ezeh Department of Community Medicine Ahmadu Bello University Zaria Nigeria

Introduction: Dysfunctional coping may negatively affect the effectiveness of highly active anti-retro-viral therapy (HAART) to achieve viral suppression. Despite its negative effects on health outcomes, dysfunctional coping remains undiagnosed and untreated in HIV patients. Therefore, it would seem important to diagnose dysfunctional coping in HIV-patients in order to enable health care providers’ plan and execute intervention and treatment strategies in a population of HIV-patients/HIV-infected persons, to reduce health problems that may be associated with it. Aims/Objectives: 1) To identify elements of dysfunctional coping in HIV-patients. 2) To screen HIV-infective persons for dysfunctional coping. 3) To assess prevalence of dysfunctional coping among HIV-patients. 4) To assess association of duration of HIV-infection (survivability) with dysfunctional coping in HIV-infected persons. Methods: A cross-sectional descriptive study was carried out, to diagnose dysfunctional coping in HIV-infected patients. Subjects were HIV patients receiving HAART at Ahmadu Bello University Teaching Hospital (ABUTH), Zaria, Nigeria. Over 110 HIV-infected persons were screened for dysfunctional coping, using 28-item Brief Cope Questionnaire. Results: After various forms of statistical analyses were performed on the data, results indicate differing prevalent rates for the four key dimensions of dysfunctional coping derived from the Brief-cope questionnaire, used to measure dysfunctional coping in this study;Denial Coping, 68 (61.8 percent), Behavioral Disengagement Coping, 46 (41.8 percent), Substance Use Coping, 24 (21.8 percent), Self-Blame Coping, 61 (55.5 percent);and composite (Integrated/Combined) dysfunctional coping, 49 (44.5 percent). A total prevalence rate of 44.5 percent was reported. Altogether more than 60 percent of the HIV patients screened positive for one dimension of dysfunctional coping or the other. The qui-square analyses revealed an association of denial coping with duration of HIV-infection/survivability. C

关键词： HIV Patients HAART diagnosing Dysfunctional Coping

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Infrastructure of Synchrotronic Biosensor Based on Semiconductor Device Fabrication for Tracking, Monitoring, Imaging, Measuring, diagnosing and Detecting Cancer Cells

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Semiconductor Science and Information Devices 2019年第2期1卷 29-57页

作者： Alireza Heidari Faculty of Chemistry California South University14731 Comet St.IrvineCA 92604USA American International Standards Institute IrvineCA 3800USA

Copper Zinc Antimony Sulfide(CZAS)is derived from Copper Antimony Sulfide(CAS),a famatinite class of compound.In the current paper,the first step for using Copper,Zinc,Antimony and Sulfide as materials in manufacturing synchrotronic biosensor-namely increasing the sensitivity of biosensor through creating Copper Zinc Antimony Sulfide,CZAS(Cu1.18Zn0.40Sb1.90S7.2)semiconductor and using it instead of Copper Tin Sulfide,CTS(Cu2SnS3)for tracking,monitoring,imaging,measuring,diagnosing and detecting cancer cells,is evaluated.Further,optimization of tris(2,2'-bipyridyl)ruthenium(II)(Ru(bpy)32+)concentrations and Copper Zinc Antimony Sulfide,CZAS(Cu1.18Zn0.40Sb1.90S7.2)semiconductor as two main and effective materials in the intensity of synchrotron for tracking,monitoring,imaging,measuring,diagnosing and detecting cancer cells are considered so that the highest sensitivity obtains.In this regard,various concentrations of two materials were prepared and photon emission was investigated in the absence of cancer cells.On the other hand,ccancer diagnosis requires the analysis of images and attributes as well as collecting many clinical and mammography variables.In diagnosis of cancer,it is important to determine whether a tumor is benign or malignant.The information about cancer risk prediction along with the type of tumor are crucial for patients and effective medical decision making.An ideal diagnostic system could effectively distinguish between benign and malignant cells;however,such a system has not been created yet.In this study,a model is developed to improve the prediction probability of cancer.It is necessary to have such a prediction model as the survival probability of cancer is high when patients are diagnosed at early stages.

关键词： Synchrotronic Biosensor Copper Zinc Antimony Sulfide CZAS(Cu1.18Zn0.40Sb1.90S7.2)Semiconductor Photomultiplier Semiconductor Device Tracking Monitoring Imaging Measuring diagnosing Detecting Cancer Cells Tris(2,2'-bipyridyl)ruthenium(II)(Ru(bpy)32+)

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Teaching Strategy on diagnosing and Removing Automobile’s Failure

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Journal of Contemporary Educational Research 2020年第3期4卷 11-13页

作者： Huimin Zhao Hong Shen Chun Chang Department of Automotive Engineering Military Transportation UniversityTianjin300161China

:This article first analyzes the problems existing in the teaching about diagnosing and removing automobile’s failure,and then discusses some specific teaching strategies on diagnosing and removing automobile’s fail... 详细信息

关键词： diagnosing and removing automobile’s failure Problems Teaching methods Practical teaching

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Fault diagnosing System of Steam Generator for Nuclear Power Plant Based on Fuzzy Neural Networks

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Journal of Marine Science and Application 2002年第1期1卷 41-46页

作者： Ming-Yu Fu Xin-Qian Bian Ji Shi

All kinds of reasons are analysed in theory and a fault repository combined with local expert experiences is establishedaccording to the structure and the operation characteristic of steam generator in this paper. At the same time, Kohonen algo-rithm is used for fault diagnoses system based on fuzzy neural networks. Fuzzy arithmetic is inducted into neural networks tosolve uncertain diagnosis induced by uncertain knowledge. According to its self-association in the course of default diagnosis. thesystem is provided with non-supervise, self-organizing, self-learning, and has strong cluster ability and fast cluster velocity.

关键词： neural network steam generator fuzzy fault diagnosing

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Translating Interdisciplinary Research on Language Learning into Identifying Specific Learning Disabilities in Verbally Gifted and Average Children and Youth

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Journal of Behavioral and Brain Science 2017年第6期7卷 227-246页

作者： Ruby Dawn Lyman Elizabeth Sanders Robert D. Abbott Virginia W. Berninger Learning Sciences and Human Development University of Washington Seattle WA USA Quantitative Methods and Measurement University of Washington Seattle WA USA

The current research was grounded in prior interdisciplinary research that showed cognitive ability (verbal ability for translating cognitions into oral language) and multiple-working memory endophenotypes (behavioral markers of genetic or brain bases of language learning) predict reading and writing achievement in students with and without specific learning disabilities in written language (SLDs-WL). Results largely replicated prior findings that verbally gifted with dyslexia score higher on reading and writing achievement than those with average verbal ability but not on endophenotypes. The current study extended that research by comparing those with and without SLDs-WL with assessed verbal ability held constant. The verbally gifted without SLDs-WL (n = 14) scored higher than the verbally gifted with SLDs-WL (n = 27) on six language skills (oral sentence construction, best and fastest handwriting in copying, single real word oral reading accuracy, oral pseudoword reading accuracy and rate) and four endophenotypes (orthographic and morphological coding, orthographic loop, and switching attention). The verbally average without SLDs-WL (n = 6) scored higher than the verbally average with SLDs-WL (n = 22) on four language skills (best and fastest hand-writing in copying, oral pseudoword reading accuracy and rate) and two endophenotypes (orthographic coding and orthographic loop). Implications of results for translating interdisciplinary research into flexible definitions for assessment and instruction to serve students with varying verbal abilities and language learning and endophenotype profiles are discussed along with directions for future research.

关键词： Defining Specific Learning Disabilities (SLDs) diagnosing Specific Learning Disabilities in Written Language (SLDs-WL) Verbal Giftedness Multi-Component Working Memory Endophenotypes Language Learning Mechanism Translation Science for Diagnosis of SLDs

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Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in a southern Chinese city

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Frontiers of Environmental Science & Engineering 2019年第2期13卷 29-41页

作者： Xiangyang Ye Jian’e Zuo Ruohan Li Yajiao Wang Lili Gan Zhonghan Yu Xiaoqing Hu State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China Thunip Corp. Ltd.27F/Section CS&T BuildingTsinghua S&T ParkBeijing 100084China China Water Environment Group.10F Campbell Building89th Jinbao StreetBeijing 101101China

Closed circuit television(CCTV)systems are widely used to inspect sewer pipe conditions.During the diagnosis process,the manual diagnosis of defects is time consuming,labor intensive and error prone.To assist inspectors in diagnosing sewer pipe defects on CCTV inspection images,this paper presents an image recognition algorithm that applies features extraction and machine learning approaches.An algorithm of image recognition techniques,including Hu invariant moment,texture features,lateral Fourier transform and Daubechies(DBn)wavelet transform,was used to describe the features of defects,and support vector machines were used to classify sewer pipe defects.According to the inspection results,seven defects were defined;the diagnostic system was applied to a sewer pipe system in a southern city of China,and 28,760 m of sewer pipes were inspected.The results revealed that the classification accuracies of the different defects ranged from 51.6% to 99.3%.The overall accuracy reached 84.1%.The diagnosing accuracy depended on the number of the training samples,and four fitting curves were applied to fit the data.According to this paper,the logarithmic fitting curve presents the highest coefficient of determination of 0.882,and more than 200 images need to be used for training samples to guarantee the accuracy higher than 85%.

关键词： Sewer pipe defects Defect diagnosing Image recognition Multi-features extraction Support vector machine

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