scholarly journals CAE-COVIDX: automatic covid-19 disease detection based on x-ray images using enhanced deep convolutional and autoencoder

2021 ◽  
Vol 7 (1) ◽  
pp. 49
Author(s):  
Hanafi Hanafi ◽  
Andri Pranolo ◽  
Yingchi Mao
Keyword(s):  
Confusion Matrix ◽  
Virus Detection ◽  
Image Features ◽  
World Health ◽  
X Rays ◽  
X Ray ◽  
First Case ◽  
Long Time ◽  
Novel Method ◽  
Health Organization

Since the first case in 2019, Corona Virus has been spreading all over the world. World Health Organization (WHO) announced that COVID-19 had become an international pandemic. There is an essential section to handle the spreading of the virus by immediate virus detection for patients. Traditional medical detection requires a long time, a specific laboratory, and a high cost. A method for detecting Covid-19 faster compared to common approaches, such as RT-PCR detection, is needed. The method demonstrated that it could produce an X-ray image with higher accuracy and consumed less time. We propose a novel method to extract image features and to classify COVID-19 using deep CNN combined with Autoencoder (AE) dubbed CAE-COVIDX. We evaluated and compared it with the traditional CNN and existing framework VGG16 involving 400 normal images of non-COVID19 and 400 positive COVID-19 diseases. The performance evaluation was conducted using accuracy, confusion matrix, and loss evaluation. Based on experiment results, the CAE-COVIDX framework outperforms previous methods in several testing scenarios. This framework's ability to detect Covid-19 in various nonstandard image X-rays could effectively help medical employers diagnose Covid-19 patients. It is an important factor to decrease the spreading of Covid-19 massively.

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 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.

scholarly journals Pneumonia Detection Using Convolutional Neural Networks

2021 ◽  
Vol 7 (01) ◽  
pp. 77-80
Author(s):  
Puneet Gupta
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Disease Classification ◽  
World Health ◽  
X Rays ◽  
X Ray ◽  
Life Threatening ◽  
Chest X Ray ◽  
Health Organization

Abstract— Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans commonly caused by bacteria called Streptococcus pneumoniae. One in three deaths in India is caused due to pneumonia as reported by World Health Organization (WHO). Chest X-Rays which are used to diagnose pneumonia, need expert radiotherapists for evaluation. Thus, developing an automatic system for detecting pneumonia would be beneficial for treating the disease without any delay particularly in remote areas. Due to the success of deep learning algorithms in analyzing medical images, Convolutional Neural Networks (CNNs) have gained much attention for disease classification. In addition, features learned by pre-trained CNN models on large-scale datasets are much useful in image classification tasks. In this work, we appraise the functionality of pre-trained CNN models utilized as feature-extractors followed by different classifiers for the classification of abnormal and normal chest X-Rays. We analytically determine the optimal CNN model for the purpose. Statistical results obtained demonstrates that pretrained CNN models employed along with supervised classifier algorithms can be very beneficial in analyzing chest X-ray images, specifically to detect Pneumonia. In this project Transfer learning and a CNN Model is used to detect whether the person has pneumonia or not using chest x-ray.

scholarly journals A Deep Neural Network for Detecting Coronavirus Disease Using Chest X-Ray Images

2022 ◽  
Vol 17 (2) ◽  
pp. 1-27
Author(s):  
Rajeev Kumar Gupta ◽  
Nilesh Kunhare ◽  
Rajesh Kumar Pateriya ◽  
Nikhlesh Pathik
Keyword(s):  
Preliminary Test ◽  
Vital Role ◽  
World Health ◽  
Test Accuracy ◽  
X Rays ◽  
The Novel ◽  
X Ray ◽  
Chest X Ray ◽  
Health Organization ◽  
The Comparative Study

The novel Covid-19 is one of the leading cause of death worldwide in the year 2020 and declared as a pandemic by world health organization (WHO). This virus affecting all countries across the world and 5 lakh people die as of June 2020 due to Covid-19. Due to the highly contagious nature, early detection of this virus plays a vital role to break Covid chain. Recent studies done by China says that chest CT and X-Ray image may be used as a preliminary test for Covid detection. Deep learning-based CNN model can use to detect Coronavirus automatically from the chest X-rays images. This paper proposed a transfer learning-based approach to detect Covid disease. Due to the less number of Covid chest images, we are using a pre-trained model to classify X-ray images into Covid and Normal class. This paper presents the comparative study of a various pre-trained model like VGGNet-19, ResNet50 and Inception_ResNet_V2. Experiment results show that Inception_ResNet_V2 gives the better result as compare to VGGNet and ResNet model with training and test accuracy of 99.26 and 94, respectively.

scholarly journals Effet des Perturbations Électriques sur les Appareils de Radiologie À Rayons X À Haute Fréquence (HF) Alimentés par le Réseau Électrique Camerounais: Cas des Coupures D’Énergie

2021 ◽  
Vol 17 (37) ◽  
pp. 156
Author(s):  
Kpalang Docta Basile ◽  
Onanena Raïssa ◽  
Ntsama Eloundou Pascal ◽  
Ndarwe Djakba ◽  
Ele Pierre ◽  
...  
Keyword(s):  
Renewable Energy Sources ◽  
World Health ◽  
Electrical Network ◽  
X Rays ◽  
The South ◽  
X Ray ◽  
Electrical Grid ◽  
Risk Of Injury ◽  
Recorded Data ◽  
Health Organization

L’organisation Mondiale de la Santé estime que plus d’un tiers des pannes des appareils médicaux utilisés dans les pays du Sud sont causées par un réseau défaillant. Le présent article porte sur l’effet des coupures d’énergie électrique sur les appareils de radiologie à rayons X à haute fréquence, alimentés par le réseau électrique Camerounais. Des mesures des grandeurs électriques aux bornes de onze appareils de radiologie ont été effectuées à l’aide d’un analyseur de réseau et d’un kVp mètre. Le logiciel d’analyse de données Win-PQ a permis d’analyser les données enrégistrées. Les résultats montrent que, des coupures ayant eu lieu pendant la production des rayonnements X, engendrent des surtensions, ce qui augmente la quantité de rayonnement irradié au patient avec risques de blessures et détérioration d’appareillage. Ces surtensions sont d’amplitudes plus élevées lorsque les appareils fonctionnent en hyper résonance. Ceci souligne la nécessité de revoir les normes de conception et de fabrication des appareils de radiologie utilisés dans les pays du Sud dont le réseau électrique est soumis en permanence à des coupures. L’une des solutions envisageable serait de recourir aux sources d’énergies renouvelables hors réseau. The World Health Organization estimates that more than one third of the breakdowns of medical equipment used in the countries of the South are caused by a faulty network. This paper investigates the effect of power outages on high-frequency X-ray machines powered by the Cameroonian electrical grid. Measurements of electrical quantities at the terminals of eleven X-ray machines were made using a network analyzer and a kVp meter. Win-PQ data analysis software was used to analyze the recorded data. The results show that power cuts during the production of X-rays cause overvoltages, which increase the intensity of radiation delivered to the patient, and also the risk of injury and damage to the equipment. These surges are of higher amplitudes when the equipment is operating in hyper-resonance. This underlines the need to review the design and manufacturing standards of X-ray equipment used in countries in the South where the electrical network is permanently subject to power cuts. One of the solutions recommended would be the use of renewable energy sources off-grid system.

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.

scholarly journals Progression and Trends in Virus from Influenza A to COVID-19: An Overview of Recent Studies

Viruses ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1145
Author(s):  
Hakimeh Baghaei Daemi ◽  
Muhammad Fakhar-e-Alam Kulyar ◽  
Xinlin He ◽  
Chengfei Li ◽  
Morteza Karimpour ◽  
...  
Keyword(s):  
Influenza A ◽  
H1n1 Virus ◽  
World Health ◽  
H1n1 Pandemic ◽  
Global Pandemic ◽  
First Case ◽  
Influenza A H1n1 ◽  
The World ◽  
Novel Coronavirus ◽  
Health Organization

Influenza is a highly known contagious viral infection that has been responsible for the death of many people in history with pandemics. These pandemics have been occurring every 10 to 30 years in the last century. The most recent global pandemic prior to COVID-19 was the 2009 influenza A (H1N1) pandemic. A decade ago, the H1N1 virus caused 12,500 deaths in just 19 months globally. Now, again, the world has been challenged with another pandemic. Since December 2019, the first case of a novel coronavirus (COVID-19) infection was detected in Wuhan. This infection has risen rapidly throughout the world; even the World Health Organization (WHO) announced COVID-19 as a worldwide emergency to ensure human health and public safety. This review article aims to discuss important issues relating to COVID-19, including clinical, epidemiological, and pathological features of COVID-19 and recent progress in diagnosis and treatment approaches for the COVID-19 infection. We also highlight key similarities and differences between COVID-19 and influenza A to ensure the theoretical and practical details of COVID-19.

scholarly journals Probiotics: A Promising Candidate for Management of Colorectal Cancer

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3178
Author(s):  
Ashutosh Tripathy ◽  
Jayalaxmi Dash ◽  
Sudhakar Kancharla ◽  
Prachetha Kolli ◽  
Deviyani Mahajan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Treatment Strategies ◽  
Intestinal Barrier ◽  
World Health ◽  
Intestinal Barrier Function ◽  
Cancer Type ◽  
Prevention Therapy ◽  
Long Time ◽  
Health Organization ◽  
Cancerous Cells

Colorectal cancer (CRC) is the World’s third most frequently diagnosed cancer type. It accounted for about 9.4% mortality out of the total incidences of cancer in the year 2020. According to estimated facts by World Health Organization (WHO), by 2030, 27 million new CRC cases, 17 million deaths, and around 75 million people living with the disease will appear. The facts and evidence that establish a link between the intestinal microflora and the occurrence of CRC are quite intuitive. Current shortcomings of chemo- and radiotherapies and the unavailability of appropriate treatment strategies for CRC are becoming the driving force to search for an alternative approach for the prevention, therapy, and management of CRC. Probiotics have been used for a long time due to their beneficial health effects, and now, it has become a popular candidate for the preventive and therapeutic treatment of CRC. The probiotics adopt different strategies such as the improvement of the intestinal barrier function, balancing of natural gut microflora, secretion of anticancer compounds, and degradation of carcinogenic compounds, which are useful in the prophylactic treatment of CRC. The pro-apoptotic ability of probiotics against cancerous cells makes them a potential therapeutic candidate against cancer diseases. Moreover, the immunomodulatory properties of probiotics have created interest among researchers to explore the therapeutic strategy by activating the immune system against cancerous cells. The present review discusses in detail different strategies and mechanisms of probiotics towards the prevention and treatment of CRC.

Dark-Field X-Ray Microscopy of Immunogold-Labeled Cells

1996 ◽  
Vol 2 (2) ◽  
pp. 53-62 ◽  
Author(s):  
Henry N. Chapman ◽  
Jenny Fu ◽  
Chris Jacobsen ◽  
Shawn Williams
Keyword(s):  
Colloidal Gold ◽  
Dark Field Image ◽  
Dark Field ◽  
X Rays ◽  
X Ray ◽  
Scanning Transmission ◽  
A Cell ◽  
Specific Antigens ◽  
Genetic Sequences ◽  
Novel Method

The methods of immunolabeling make visible the presence of specific antigens, proteins, genetic sequences, or functions of a cell. In this paper we present examples of imaging immunolabels in a scanning transmission x-ray microscope using the novel method of dark-field contrast. Colloidal gold, or silver-enhanced colloidal gold, is used as a label, which strongly scatters x-rays. This leads to a high-contrast dark-field image of the label and reduced radiation dose to the specimen. The x-ray images are compared with electron micrographs of the same labeled, unsectioned, whole cell. It is verified that the dark-field x-ray signal is primarily due to the label and the bright-field x-ray signal, showing absorption due to carbon, is largely unaffected by the label. The label can be well visualized even when it is embedded in or laying behind dense material, such as the cell nucleus. The resolution of the images is measured to be 60 nm, without the need for computer processing. This figure includes the x-ray microscope resolution and the accuracy of the label positioning. The technique should be particularly useful for the study of relatively thick (up to 10 μm), wet, or frozen hydrated specimens.

Impact OF Covid-19 on Several Industries in Context with the Dialysis Industry

2021 ◽  
Vol 8 (2) ◽  
pp. 01-03
Author(s):  
Ashish Gujrathi
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
World Health Organization ◽  
World Health ◽  
Healthcare Sector ◽  
Growth Opportunities ◽  
Wuhan City ◽  
First Case ◽  
The World ◽  
New Challenges ◽  
Health Organization

Coronavirus (COVID-19) was recognized in late December in Hubei province of Wuhan city in China. This highly contagious disease, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is transmitted from humans to humans. After the first case in Wuhan, the disease rapidly spread to other parts of the globe. On March 11, 2020, the World Health Organization (WHO) made an assessment that COVID-19 can be characterized as a pandemic. Thus, social-distancing became an important measure to stop the spread of this disease. Various countries across the world adopted nationwide lockdowns. This led to a completely new scenario for the world, where every business in each industry faced new challenges and witnessed new opportunities. Similarly, the medical personal protective industry, a vital part of the healthcare sector, also witnessed new growth opportunities.

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