scholarly journals Automated Diagnosis of Chest X-Ray for Early Detection of COVID-19 Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ebrahim Mohammed Senan ◽  
Ali Alzahrani ◽  
Mohammed Y. Alzahrani ◽  
Nizar Alsharif ◽  
Theyazn H. H. Aldhyani
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Area Under The Curve ◽  
World Health ◽  
X Ray ◽  
Medical Sector ◽  
The World ◽  
Health Organization ◽  
Many Sources ◽  
Sensitivity Specificity

In March 2020, the World Health Organization announced the COVID-19 pandemic, its dangers, and its rapid spread throughout the world. In March 2021, the second wave of the pandemic began with a new strain of COVID-19, which was more dangerous for some countries, including India, recording 400,000 new cases daily and more than 4,000 deaths per day. This pandemic has overloaded the medical sector, especially radiology. Deep-learning techniques have been used to reduce the burden on hospitals and assist physicians for accurate diagnoses. In our study, two models of deep learning, ResNet-50 and AlexNet, were introduced to diagnose X-ray datasets collected from many sources. Each network diagnosed a multiclass (four classes) and a two-class dataset. The images were processed to remove noise, and a data augmentation technique was applied to the minority classes to create a balance between the classes. The features extracted by convolutional neural network (CNN) models were combined with traditional Gray-level Cooccurrence Matrix (GLCM) and Local Binary Pattern (LBP) algorithms in a 1-D vector of each image, which produced more representative features for each disease. Network parameters were tuned for optimum performance. The ResNet-50 network reached accuracy, sensitivity, specificity, and Area Under the Curve (AUC) of 95%, 94.5%, 98%, and 97.10%, respectively, with the multiclasses (COVID-19, viral pneumonia, lung opacity, and normal), while it reached accuracy, sensitivity, specificity, and AUC of 99%, 98%, 98%, and 97.51%, respectively, with the binary classes (COVID-19 and normal).

scholarly journals Automatically identify COVID-19 and Pneumonia patients using Deep learning in Chest X-rays image

2021 ◽  
Vol 12 (3) ◽  
pp. 011-019
Author(s):  
Haris Uddin Sharif ◽  
Shaamim Udding Ahmed
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Infected Patient ◽  
World Health ◽  
X Rays ◽  
Popular Approach ◽  
X Ray ◽  
The World ◽  
Chest X Ray ◽  
Health Organization

At the end of 2019, a new kind of coronavirus (SARS-CoV-2) suffered worldwide and has become the pandemic coronavirus (COVID-19). The outbreak of this virus let to crisis around the world and kills millions of people globally. On March 2020, WHO (World Health Organization) declared it as pandemic disease. The first symptom of this virus is identical to flue and it destroys the human respiratory system. For the identification of this disease, the first key step is the screening of infected patients. The easiest and most popular approach for screening of the COVID-19 patients is chest X-ray images. In this study, our aim to automatically identify the COVID-19 and Pneumonia patients by the X-ray image of infected patient. To identify COVID19 and Pneumonia disease, the convolution Neural Network was training on publicly available dataset on GitHub and Kaggle. The model showed the 98% and 96% training accuracy for three and four classes respectively. The accuracy scores showed the robustness of both model and efficiently deployment for identification of COVID-19 patients.

Determination of Heavy Metals in Damascus Drinking Water Using Total Reflection X-Ray Fluorescence

1999 ◽  
Vol 34 (2) ◽  
pp. 305-316 ◽  
Author(s):  
E.H. Bakraji ◽  
J. Karajo
Keyword(s):  
Trace Elements ◽  
Drinking Water ◽  
Total Reflection ◽  
World Health ◽  
X Ray ◽  
Quality Guidelines ◽  
The World ◽  
The Mean ◽  
Health Organization

Abstract Total reflection X-ray fluorescence spectrometry and chemical preconcentration have been applied for multi-elemental analysis of Damascus drinking water. Water was taken directly from taps of several city sectors and analyzed for the following trace elements: Ti, V, Cr, Fe, Co, Ni, Cu, Zn, Se and Pb. The detection limits were found to be in the range of 0.1 to 0.4 µg/L. The mean levels of trace elements in the Damascus drinking water were below the World Health Organization drinking water quality guidelines.

scholarly journals Counterfeit drugs as a global threat to health

2011 ◽  
Vol 64 (5-6) ◽  
pp. 285-290 ◽  
Author(s):  
Svetlana Golocorbin-Kon ◽  
Momir Mikov
Keyword(s):  
World Health Organization ◽  
Health Professionals ◽  
World Health ◽  
Sources Of Information ◽  
Counterfeit Drugs ◽  
Counterfeit Medicines ◽  
The World ◽  
Health Organization ◽  
Global Threat ◽  
Many Sources

According to the World Health Organization, counterfeit medicines are medicines that are mislabeled deliberately and fraudulently regarding their identity and/or source. All kinds of medicines have been counterfeited, both branded and generic ones. Counterfeit medicines may include products containing correct or wrong ingredients; without active or with insufficiently or over-active ingredients, or with fake packaging. Many sources of information have been explored, including reports from the national medicine regulatory authorities, pharmaceutical companies and literature data. Since the time counterfeit drugs first appeared, they have become more sophisticated and more difficult to be detected. The World Health Organization estimate is that up to 1% of medicines available in the developed world are likely to be counterfeit. This figure rises to 10% globally, although in some developing countries it is 50%. The World Health Organization estimate is that 50% of medicines available via the internet are counterfeit. The knowledge about counterfeit drugs should be used to educate students of pharmacy and medicine, health professionals and patients. The most important players in campaign against counterfeit medicines are health professionals. Pharmacists and doctors should stay vigilant and report suspicious products, and consider counterfeits as a possible cause of adverse reactions or therapeutic failure. Patients should inform their pharmacists and doctors if they suspect any irregularity concerning their medication, if they experience side effects or a decrease in beneficial effect. The crucial step in the prevention of counterfeit medicines is to get supplied from reliable sources, i.e. licensed pharmacies.

scholarly journals An Explainable approach to Deep Learning from CT-scans for Covid Identification

2021 ◽  
Author(s):  
Eduardo Soares ◽  
Plamen Angelov ◽  
Ziyang Zhang
Keyword(s):  
Deep Learning ◽  
World Health ◽  
Learning Approaches ◽  
Heat Map ◽  
Algorithm Performance ◽  
Decision Mechanism ◽  
The World ◽  
Public Health Officials ◽  
Decision Structure ◽  
Health Organization

The Covid-19 disease has spread widely over the whole world since the beginning of 2020. Following the epidemic which started in Wuhan, China on January 30, 2020 the World Health Organization (WHO) declared a global health emergency and a pandemic. Researchers of different disciplines work along with public health officials to understand the SARS-CoV-2 pathogenesis and jointly with the policymakers urgently develop strategies to control the spread of this new disease. Recent findings have observed specific image patterns from computed tomography (CT) for patients infected by SARS-CoV-2 which are distinct from the other pulmonary diseases. In this paper, we propose an explainable-by-design that has an integrated image segmentation mechanism based on SLIC that improves the algorithm performance and the interpretability of the resulting model. In order to evaluate the proposed approach, we used the SARS-CoV-2 CT scan dataset that we published recently and has been widely used in the literature. The proposed Super-xDNN could obtain statistically better results than traditional deep learning approaches as DenseNet-201 and Resnet-152. Furthermore, it also improved the explainability and interpretability of its decision mechanism when compared with the xDNN basis approach that uses the whole image as prototype. The segmentation mechanism of Super-xDNN favored a decision structure that is more close to the human logic. Moreover, it also allowed the provision of new insights as a heat-map which highlights the areas with highest similarities with Covid-19 prototypes, and an estimation of the area affected by the disease.

scholarly journals Deep learning for classification of pediatric chest radiographs by WHO’s standardized methodology

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253239
Author(s):  
Yiyun Chen ◽  
Craig S. Roberts ◽  
Wanmei Ou ◽  
Tanaz Petigara ◽  
Gregory V. Goldmacher ◽  
...  
Keyword(s):  
Deep Learning ◽  
World Health Organization ◽  
Characteristic Curve ◽  
Learning Model ◽  
Observer Agreement ◽  
World Health ◽  
Widespread Application ◽  
The World ◽  
Health Organization ◽  
Deep Learning Model

Background The World Health Organization (WHO)-defined radiological pneumonia is a preferred endpoint in pneumococcal vaccine efficacy and effectiveness studies in children. Automating the WHO methodology may support more widespread application of this endpoint. Methods We trained a deep learning model to classify pneumonia CXRs in children using the World Health Organization (WHO)’s standardized methodology. The model was pretrained on CheXpert, a dataset containing 224,316 adult CXRs, and fine-tuned on PERCH, a pediatric dataset containing 4,172 CXRs. The model was then tested on two pediatric CXR datasets released by WHO. We also compared the model’s performance to that of radiologists and pediatricians. Results The average area under the receiver operating characteristic curve (AUC) for primary endpoint pneumonia (PEP) across 10-fold validation of PERCH images was 0.928; average AUC after testing on WHO images was 0.977. The model’s classification performance was better on test images with high inter-observer agreement; however, the model still outperformed human assessments in AUC and precision-recall spaces on low agreement images. Conclusion A deep learning model can classify pneumonia CXR images in children at a performance comparable to human readers. Our method lays a strong foundation for the potential inclusion of computer-aided readings of pediatric CXRs in vaccine trials and epidemiology studies.

scholarly journals Deep Learning Detection of COVID-19, Temporal Variation and its Link with Temperature in Nigeria

2021 ◽  
pp. 55-62
Author(s):  
Ebiendele Eromosele Precious
Keyword(s):  
Deep Learning ◽  
Health Workers ◽  
World Health ◽  
Religious Activities ◽  
X Ray ◽  
Global Pandemic ◽  
First Case ◽  
Average Accuracy ◽  
Maximum Temperatures ◽  
Health Organization

COVID-19 was announced as a global pandemic on 11 March 2020 by the World Health Organization due to its spread globally.  Nigeria recorded its first case on 27 February 2020. Since then, it has spread to all parts of the country. In this paper we study the effectiveness and skill performance of deep learning architectures in assisting health workers in detecting COVID-19 infected patient through X-ray images. Analytical deductions obtained from 500 X-ray images of both infected and non-infected patients confirmed that our proposed model InceptionV3 is effective in detecting COVID-19 and attain an average accuracy of 92%. The relationship or link between the COVID-19 daily occurrence and two meteorological variables (minimum and maximum temperatures) are further assessed. The result also indicated that the cases recorded in Wednesdays and Fridays are observed to be higher than other days which usually coincide with either religious activities or market days in the country, while a progressively decline in weekday cases is observed towards the weekend with Sundays (ranging from 152 to 280 cases) having the lowest cases. The study further indicated statistically that COVID-19 daily cases significantly decline when maximum and minimum temperature are increasing (-0.79 and -0.44 correlation coefficient).

scholarly journals Designing for deep learning in the context of digital and social media

Comunicar ◽  
2019 ◽  
Vol 27 (58) ◽  
pp. 9-18 ◽  
Author(s):  
James-Paul Gee ◽  
Moisés Esteban-Guitart
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
World Health ◽  
Human Beings ◽  
Redes Sociales ◽  
Living Things ◽  
The World ◽  
Mind And Body ◽  
Health Organization

There is today a great deal of controversy over digital and social media. Even leaders in the tech industry are beginning to decry the time young people spend on smartphones and social networks. Recently, the World Health Organization proposed adding “gaming disorder” to its official list of diseases, defining it as a pattern of gaming behavior so severe that it takes “precedence over other life interests”. At the same time, many others have celebrated the positive properties of video games, social media, and social networks. This paper argues that a deeper understanding of human beings is needed to design for deep learning. For the purposes of this study “design for deep learning” means helping people matter and find meaning in ways that make them and others healthy in mind and body, while improving the state of the world for all living things, with due respect for truth, sensation, happiness, imagination, individuality, diversity, and the future. In particular, fifteen features related to human nature are suggested based on recent scientific developments to answer the question: What is a human being? Consequently, proposals that are linked to learning and transformation, as well as social improvement, should fit with the ways in which humans, as specific sorts of biological and social creatures, learn best (or can learn at all) and can change for the better. En la actualidad existe una nutrida controversia en relación a los medios de comunicación sociales y digitales que ha llevado, incluso, a censurar la utilización de las redes sociales y los móviles por parte de líderes en la industria tecnológica. En este sentido, la Organización Mundial para la Salud ha propuesto añadir el «desorden del juego» a su listado de enfermedades, definiéndolo como un modelo de comportamiento de juego tan severo que se impone como «preferencia sobre otros intereses». Al mismo tiempo, distintos académicos han enfatizado los aspectos positivos derivados de las redes sociales y los videojuegos. En este artículo se argumenta que es necesaria una mejor comprensión del ser humano para poder implementar lo que aquí se define como diseño para el aprendizaje profundo. El «diseño para el aprendizaje profundo» está encaminado al reconocimiento de las personas y el desarrollo de sentidos saludables, individual y colectivamente, así como la mejora, en general, del estado del mundo para todos los seres vivos, según principios de verdad, felicidad, imaginación, individualidad, diversidad y futuro. En particular, se sugieren quince características basadas en desarrollos científicos que responden a la pregunta: ¿Qué es un ser humano? Consecuentemente, propuestas vinculadas al aprendizaje y la transformación y mejora social deben ser coherentes con dichas características que permiten definir cómo las personas, en tanto que organismos biológicos y sociales, aprenden o pueden aprender óptimamente, así como cambiar para mejorar.

scholarly journals DETERMINATION OF LEAD FROM GOLD ORE USING PROTON INDUCED X-RAY EMISSION TECHNIQUE

2021 ◽  
Vol 5 (1) ◽  
pp. 490-494
Author(s):  
A. Bello
Keyword(s):  
Research Work ◽  
World Health ◽  
Elemental Content ◽  
Northern Nigeria ◽  
X Ray ◽  
Mining Sites ◽  
The World ◽  
Health Organization ◽  
Pb Concentration

The impetus for this research work arose from alleged signs of Lead (Pb) poisoning from Medicines Sans Frontiers (Doctors without Borders).These poisonings were narrowed down to areas of solid minerals mining and extraction in Northern Nigeria. The aim of this research work is to identify mining Sites with ores having high Pb concentration. Fifteen samples were collected from areas located at approximately latitudes 𝟶𝟶70𝟶8.69𝟶ˈE and longitudes 𝟶90 34ˈ224ˈˈN and interrogated using Proton induced X-ray emission (PIXE) technique for their elemental content. PIXE was chosen because of its sample nondestructive and it does not contaminate the environment. The result obtained varied between 24.3 – 632303.3 ppm. The world Health Organization recommends that sites with Pb concentration above 400 ppm are inimical to human health and ordered that children be evacuated from such areas. Exposure to Pb poisoning may cause anemia, weakness, and Kidney and brain damage; particularly in children.

scholarly journals A Deep Learning Approach to Detect COVID-19 Patients from Chest X-ray Images

AI ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 418-435
Author(s):  
Khandaker Haque ◽  
Ahmed Abdelgawad
Keyword(s):  
Deep Learning ◽  
World Health ◽  
X Rays ◽  
X Ray ◽  
Global Pandemic ◽  
Proposed Model ◽  
Dental Diseases ◽  
Chest X Ray ◽  
Artery Disease ◽  
Health Organization

Deep Learning has improved multi-fold in recent years and it has been playing a great role in image classification which also includes medical imaging. Convolutional Neural Networks (CNNs) have been performing well in detecting many diseases including coronary artery disease, malaria, Alzheimer’s disease, different dental diseases, and Parkinson’s disease. Like other cases, CNN has a substantial prospect in detecting COVID-19 patients with medical images like chest X-rays and CTs. Coronavirus or COVID-19 has been declared a global pandemic by the World Health Organization (WHO). As of 8 August 2020, the total COVID-19 confirmed cases are 19.18 M and deaths are 0.716 M worldwide. Detecting Coronavirus positive patients is very important in preventing the spread of this virus. On this conquest, a CNN model is proposed to detect COVID-19 patients from chest X-ray images. Two more CNN models with different number of convolution layers and three other models based on pretrained ResNet50, VGG-16 and VGG-19 are evaluated with comparative analytical analysis. All six models are trained and validated with Dataset 1 and Dataset 2. Dataset 1 has 201 normal and 201 COVID-19 chest X-rays whereas Dataset 2 is comparatively larger with 659 normal and 295 COVID-19 chest X-ray images. The proposed model performs with an accuracy of 98.3% and a precision of 96.72% with Dataset 2. This model gives the Receiver Operating Characteristic (ROC) curve area of 0.983 and F1-score of 98.3 with Dataset 2. Moreover, this work shows a comparative analysis of how change in convolutional layers and increase in dataset affect classifying performances.

Covid-19 Detection using Deep Convolutional Neural Networks from X-Ray Images

2021 ◽  
Vol 4 (2) ◽  
pp. 139-143
Author(s):  
Abdullah Ajmal ◽  
Sundas Ibrar ◽  
Wakeel Ahmad ◽  
Syed Muhammad Adnan Shah
Keyword(s):  
Deep Learning ◽  
Global Economy ◽  
Data Augmentation ◽  
Diagnostic Techniques ◽  
Deep Convolutional Neural Networks ◽  
Data Set ◽  
Daily Lives ◽  
X Ray ◽  
The World ◽  
Novel Coronavirus

Abstract— The Novel Coronavirus generally, knows as COVID-19 which first appeared in Wuhan city of China in December 2019, spread quickly around the world and became a pandemic. It has caused an overwhelming effect on daily lives, Public health, and the global economy. Many people have been affected and have died. It is critical to control and prevent the spread of COVID-19 disease by applying quick alternative diagnostic techniques. COVID-19 cases are rising day by day around the world, the on-time diagnosis of COVID-19 patients is an increasingly long and difficult process. COVID-19 patient test kits are costly and not available for every individual in poor countries. For this purpose, screening patients with the established techniques like Chest X-ray images seems to be an effective method. This study used a deep learning data augmentation on a publicly available data set and train advanced CNN models on it. The proposed model was tested using a state-of-the-art evaluation measures and obtained better results. Our model, the COVID-19 images is available at (https://github.com/ieee8023/covid-chestxray-dataset) and for Non-COVID-19 images is available at (https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia). The maximum accuracy achieved in the validation was 96.67%. Our model of COVID-19 detection achieved an average F measure of 98%, and an Area Under Curve (AUC) of 99%. The results demonstrate that deep learning proved to be an effective and easily deployable approach for COVID-19 detection.

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