scholarly journals COVID19 detection from Radiographs: Is Deep Learning able to handle the crisis?

2020 ◽  
Author(s):  
Muhammad Saqib ◽  
Saeed Anwar ◽  
Abbas Anwar ◽  
Lars petersson ◽  
Michael Blumenstein
Keyword(s):  
Deep Learning ◽  
Viral Infection ◽  
Viral Infections ◽  
State Of The Art ◽  
World Health ◽  
Screening Tests ◽  
Early Screening ◽  
Current State ◽  
Infection Types ◽  
Health Organization

The COVID-19 is a highly contagious viral infection which played havoc on everyone's life in many different ways. According to the world health organization and scientists, more testing potentially helps governments and disease control organizations in containing the spread of the virus. The use of chest radiographs is one of the early screening tests to determine the onset of disease, as the infection affects the lungs severely. This study will investigate and automate the process of testing by using state-of-the-art CNN classifiers to detect the COVID19 infection. However, the viral could of many different types; therefore, we only regard for COVID19 while the other viral infection types are treated as non-COVID19 in the radiographs of various viral infections. The classification task is challenging due to the limited number of scans available for COVID19 and the minute variations in the viral infections. We aim to employ current state-of-the-art CNN architectures, compare their results, and determine whether deep learning algorithms can handle the crisis appropriately. All trained models are available at https://github.com/saeed-anwar/COVID19-Baselines

scholarly journals COVID19 detection from Radiographs: Is Deep Learning able to handle the crisis?

2020 ◽  
Author(s):  
Muhammad Saqib ◽  
Saeed Anwar ◽  
Abbas Anwar ◽  
Lars petersson ◽  
Michael Blumenstein
Keyword(s):  
Deep Learning ◽  
Viral Infection ◽  
Viral Infections ◽  
State Of The Art ◽  
World Health ◽  
Screening Tests ◽  
Early Screening ◽  
Current State ◽  
Infection Types ◽  
Health Organization

The COVID-19 is a highly contagious viral infection which played havoc on everyone's life in many different ways. According to the world health organization and scientists, more testing potentially helps governments and disease control organizations in containing the spread of the virus. The use of chest radiographs is one of the early screening tests to determine the onset of disease, as the infection affects the lungs severely. This study will investigate and automate the process of testing by using state-of-the-art CNN classifiers to detect the COVID19 infection. However, the viral could of many different types; therefore, we only regard for COVID19 while the other viral infection types are treated as non-COVID19 in the radiographs of various viral infections. The classification task is challenging due to the limited number of scans available for COVID19 and the minute variations in the viral infections. We aim to employ current state-of-the-art CNN architectures, compare their results, and determine whether deep learning algorithms can handle the crisis appropriately. All trained models are available at https://github.com/saeed-anwar/COVID19-Baselines

scholarly journals Understanding the relationship between viral infections and trace elements from a metallomics perspective: implications for COVID-19

Metallomics ◽  
2020 ◽  
Author(s):  
Jemmyson Romário de Jesus ◽  
Tatianny de Araújo Andrade
Keyword(s):  
Trace Elements ◽  
Viral Infection ◽  
World Health Organization ◽  
Viral Infections ◽  
World Health ◽  
The World ◽  
Novel Virus ◽  
Health Organization ◽  
The Relationship

Recently, the World Health Organization (WHO) declared a pandemic situation due to a new viral infection (COVID-19) caused by a novel virus (Sars-CoV-2).

scholarly journals Peculiarities of Allergy Diagnosis in Children

2017 ◽  
Vol 72 (1) ◽  
pp. 33-41 ◽  
Author(s):  
L. S. Namazova-Baranova ◽  
M. A. Snovskaya ◽  
I. L. Mitushin ◽  
O. V. Kozhevnikova ◽  
A. S. Batyrova
Keyword(s):  
Viral Infections ◽  
Clinical Symptoms ◽  
Allergic Diseases ◽  
World Health ◽  
Screening Tests ◽  
Allergic Reactions ◽  
Allergy Diagnosis ◽  
Allergy Diagnostics ◽  
Personalized Approach ◽  
Health Organization

Allergic disease is a serious problem in practical healthcare. Over the last 40 years there has been exponential growth in the prevalence. According to the world health organization information, allergic diseases are at the 2nd place in prevalence the children, behind the viral infections. Their frequency and severity are increasing. In this regard, the relevance of timely and skilled diagnostic allergopathology is most important. In this study the current state of the question of allergy diagnostics is considered, the international experience is summarized and the approach to the allergy diagnosis based on use of step-by-step identification of a causal and significant factor of allergic reactions is offered. On the basis of the analysis of relevance and the importance for patients of one or the other allergens (taking into account a source of allergens and age of patients) use of a step-by-step allergy diagnostics algorithm is offered. The first step is definition of clinical implications of an allergy. It means direct contact of the phisition with the patient, clarification of its complaints, clinical symptoms, medical history disease. The second step is the confirmation of IgE-dependent mechanism. It involves the using of screening tests that are selected depending on the clinical symptoms and seasonality manifestations (the screening module). The third step is to identify the source of the allergens that are most meaningful for the patient with using test panels (modules). The panels include the most common and clinically relevant triggers of allergic reactions. The fourth step is the search for an individual significant allergens, which were not included in the diagnostic modules. On the fifth step, we plan to conduct component-divided diagnostics and detect the antibodies to unique components of significant allergens. The developed diagnostics algorithm, corresponds to needs of both the adult, and children’s population and provides the personalized approach to the patients.

Contemporary Global Health Governance Actors

2018 ◽  
Author(s):  
Jeremy Youde
Keyword(s):  
Global Health ◽  
World Health ◽  
Global Health Governance ◽  
Governance System ◽  
Health Governance ◽  
Current State ◽  
Funding Agencies ◽  
Health Organization ◽  
Private Actors ◽  
Public Versus Private

While Chapter 3 focuses primarily on the evolution of global health governance, Chapter 4 pays more attention to its contemporary manifestation as a secondary institution within international society. This chapter discusses the current state of the global health governance architecture—who the important actors are, how they operate, how they have changed over the past twenty-five years, and how they illustrate the fundamental beliefs and attitudes within the global health governance system. In particular, the chapter discusses the relative balance between state-based and non-state actors, as well as public versus private actors. This chapter highlights five key players within contemporary global health governance: states; the World Health Organization; multilateral funding agencies; public–private partnerships; and non-state and private actors

scholarly journals A review of current knowledge of myeloproliferative disorders in the horse

2021 ◽  
Vol 63 (1) ◽  
Author(s):  
Katy Satué ◽  
Juan Carlos Gardon ◽  
Ana Muñoz
Keyword(s):  
Bone Marrow ◽  
Myeloid Leukemia ◽  
Current Knowledge ◽  
Myeloproliferative Neoplasms ◽  
Myeloproliferative Disorders ◽  
World Health ◽  
Chronic Granulocytic Leukemia ◽  
Current State ◽  
Multiple Cell ◽  
Health Organization

AbstractMyeloid disorders are conditions being characterized by abnormal proliferation and development of myeloid lineage including granulocytes (neutrophils, eosinophils and basophils), monocytes, erythroids, and megakaryocytes precursor cells. Myeloid leukemia, based on clinical presentation and proliferative rate of neoplastic cells, is divided into acute (AML) and myeloproliferative neoplasms (MPN). The most commonly myeloid leukemia reported in horses are AML-M4 (myelomonocytic) and AML-M5 (monocytic). Isolated cases of AML-M6B (acute erythroid leukemia), and chronic granulocytic leukemia have also been reported. Additionally, bone marrow disorders with dysplastic alterations and ineffective hematopoiesis affecting single or multiple cell lineages or myelodysplastic diseases (MDS), have also been reported in horses. MDSs have increased myeloblasts numbers in blood or bone marrow, although less than 20%, which is the minimum level required for diagnosis of AML. This review performed a detailed description of the current state of knowlegde of the myeloproliferative disorders in horses following the criteria established by the World Health Organization.

scholarly journals IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3495
Author(s):  
Shabir Hussain ◽  
Yang Yu ◽  
Muhammad Ayoub ◽  
Akmal Khan ◽  
Rukhshanda Rehman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Face Mask ◽  
World Health ◽  
Rapid Screening ◽  
Screening System ◽  
Source Of Infection ◽  
The Face ◽  
Health Organization ◽  
Vaccine Distribution

The spread of COVID-19 has been taken on pandemic magnitudes and has already spread over 200 countries in a few months. In this time of emergency of COVID-19, especially when there is still a need to follow the precautions and developed vaccines are not available to all the developing countries in the first phase of vaccine distribution, the virus is spreading rapidly through direct and indirect contacts. The World Health Organization (WHO) provides the standard recommendations on preventing the spread of COVID-19 and the importance of face masks for protection from the virus. The excessive use of manual disinfection systems has also become a source of infection. That is why this research aims to design and develop a low-cost, rapid, scalable, and effective virus spread control and screening system to minimize the chances and risk of spread of COVID-19. We proposed an IoT-based Smart Screening and Disinfection Walkthrough Gate (SSDWG) for all public places entrance. The SSDWG is designed to do rapid screening, including temperature measuring using a contact-free sensor and storing the record of the suspected individual for further control and monitoring. Our proposed IoT-based screening system also implemented real-time deep learning models for face mask detection and classification. This module classified individuals who wear the face mask properly, improperly, and without a face mask using VGG-16, MobileNetV2, Inception v3, ResNet-50, and CNN using a transfer learning approach. We achieved the highest accuracy of 99.81% while using VGG-16 and the second highest accuracy of 99.6% using MobileNetV2 in the mask detection and classification module. We also implemented classification to classify the types of face masks worn by the individuals, either N-95 or surgical masks. We also compared the results of our proposed system with state-of-the-art methods, and we highly suggested that our system could be used to prevent the spread of local transmission and reduce the chances of human carriers of COVID-19.

scholarly journals A Survey of Graphical Page Object Detection with Deep Neural Networks

2021 ◽  
Author(s):  
Jwalin Bhatt ◽  
Khurram Azeem Hashmi ◽  
Muhammad Zeshan Afzal ◽  
Didier Stricker
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Conceptual Understanding ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Learning Approaches ◽  
Document Images ◽  
Essential Information ◽  
Current State ◽  
High Level

In any document, graphical elements like tables, figures, and formulas contain essential information. The processing and interpretation of such information require specialized algorithms. Off-the-shelf OCR components cannot process this information reliably. Therefore, an essential step in document analysis pipelines is to detect these graphical components. It leads to a high-level conceptual understanding of the documents that makes digitization of documents viable. Since the advent of deep learning, the performance of deep learning-based object detection has improved many folds. In this work, we outline and summarize the deep learning approaches for detecting graphical page objects in the document images. Therefore, we discuss the most relevant deep learning-based approaches and state-of-the-art graphical page object detection in document images. This work provides a comprehensive understanding of the current state-of-the-art and related challenges. Furthermore, we discuss leading datasets along with the quantitative evaluation. Moreover, it discusses briefly the promising directions that can be utilized for further improvements.

scholarly journals Design and reporting considerations for genetic screening tests

2019 ◽  
Author(s):  
Jill Hagenkord ◽  
Birgit Funke ◽  
Emily Qian ◽  
Madhuri Hegde ◽  
Kevin B Jacobs ◽  
...  
Keyword(s):  
Genetic Screening ◽  
World Health ◽  
Screening Tests ◽  
Clinical Indication ◽  
Test Design ◽  
Design Strategies ◽  
Predictive Values ◽  
Inherited Diseases ◽  
Health Communities ◽  
Health Organization

Testing asymptomatic individuals for unsuspected conditions is not new to the medical and public health communities and protocols to develop screening tests are well-established. However, the application of screening principles to inherited diseases presents unique challenges. Unlike most screening tests, the natural history and disease prevalence of most rare inherited diseases in an unselected population are unknown. It is difficult or impossible to obtain a “truth set” cohort for clinical validation studies. As a result, it is not possible to accurately calculate clinical positive and negative predictive values for “likely pathogenic” genetic variants, which are commonly returned in genetic screening assays. In addition, many of the genetic conditions included in screening panels do not have clinical confirmatory tests. All of these elements are typically required to justify the development of a screening test, according to the World Health Organization screening principles. Nevertheless, as the cost of DNA sequencing continues to fall, more individuals are opting to undergo genomic testing in the absence of a clinical indication. Despite the challenges, reasonable estimates can be deduced and used to inform test design strategies. Here, we review test design principles and apply them to genetic screening.

scholarly journals Design and reporting considerations for genetic screening tests

2019 ◽  
Author(s):  
Jill Hagenkord ◽  
Birgit Funke ◽  
Emily Qian ◽  
Madhuri Hegde ◽  
Kevin B Jacobs ◽  
...  
Keyword(s):  
Genetic Screening ◽  
World Health ◽  
Screening Tests ◽  
Clinical Indication ◽  
Test Design ◽  
Design Strategies ◽  
Predictive Values ◽  
Inherited Diseases ◽  
Health Communities ◽  
Health Organization

Testing asymptomatic individuals for unsuspected conditions is not new to the medical and public health communities and protocols to develop screening tests are well-established. However, the application of screening principles to inherited diseases presents unique challenges. Unlike most screening tests, the natural history and disease prevalence of most rare inherited diseases in an unselected population are unknown. It is difficult or impossible to obtain a “truth set” cohort for clinical validation studies. As a result, it is not possible to accurately calculate clinical positive and negative predictive values for “likely pathogenic” genetic variants, which are commonly returned in genetic screening assays. In addition, many of the genetic conditions included in screening panels do not have clinical confirmatory tests. All of these elements are typically required to justify the development of a screening test, according to the World Health Organization screening principles. Nevertheless, as the cost of DNA sequencing continues to fall, more individuals are opting to undergo genomic testing in the absence of a clinical indication. Despite the challenges, reasonable estimates can be deduced and used to inform test design strategies. Here, we review test design principles and apply them to genetic screening.

scholarly journals A Robust Context-Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Juan F. Ramirez Rochac ◽  
Nian Zhang ◽  
Lara A. Thompson ◽  
Tolessa Deksissa
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Mineral Exploration ◽  
Classification Models ◽  
Noise Resistance ◽  
Deep Convolutional Neural Networks ◽  
Current State ◽  
Feature Augmentation ◽  
Active Research ◽  
Hyperspectral Classification

Hyperspectral imaging is an area of active research with many applications in remote sensing, mineral exploration, and environmental monitoring. Deep learning and, in particular, convolution-based approaches are the current state-of-the-art classification models. However, in the presence of noisy hyperspectral datasets, these deep convolutional neural networks underperform. In this paper, we proposed a feature augmentation approach to increase noise resistance in imbalanced hyperspectral classification. Our method calculates context-based features, and it uses a deep convolutional neuronet (DCN). We tested our proposed approach on the Pavia datasets and compared three models, DCN, PCA + DCN, and our context-based DCN, using the original datasets and the datasets plus noise. Our experimental results show that DCN and PCA + DCN perform well on the original datasets but not on the noisy datasets. Our robust context-based DCN was able to outperform others in the presence of noise and was able to maintain a comparable classification accuracy on clean hyperspectral images.

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