scholarly journals Automated Detection of COVID-19 using Deep Learning

2021 ◽  
Vol 10 (2) ◽  
pp. , 824-830
Keyword(s):  
Public Health ◽  
Deep Learning ◽  
Global Economy ◽  
Imaging Techniques ◽  
Automated Detection ◽  
Diagnostic Tools ◽  
Radiological Imaging ◽  
Daily Lives ◽  
Learning Techniques ◽  
The World

Corona virus 2019 (COVID-2019), has first appeared in Wuhan, China in December 2019, spread around the world rapidly causing thousands of fatalities. It is caused a devastating result in our daily lives, public health, and also the global economy. It is important to sight the positive cases as early as possible therefore forestall any unfoldment of this epidemic and to quickly treat affected patients. The necessity for auxiliary diagnostic tools has increased as there aren't any accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Coupling deep learning techniques with radiological imaging may end up within the accurate detection of this disease. This assistance will help to beat the matter of an absence of specialized physicians in the remote villages.

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.

scholarly journals A Potential Novel COVID-19 Vaccine With RBD-HR1/HR2 Hexamer Structure

2021 ◽  
Author(s):  
Hongbo Liu ◽  
Xiang Gao ◽  
Guoyong Wang ◽  
Jianjun Zhang ◽  
Jiajie Zhou ◽  
...  
Keyword(s):  
Public Health ◽  
Recombinant Protein ◽  
Global Economy ◽  
Neutralizing Antibody ◽  
Scaling Up ◽  
Heptad Repeat ◽  
The Novel ◽  
Health Consequence ◽  
The World ◽  
Frequent Mutations

The COVID-19 pandemic and the continued spreading of the SARS-CoV-2 variants have brought a grave public health consequence and severely devastated the global economy with recessions. Vaccination is considered as one of the most promising and efficient methods to end the COVID-19 pandemic and mitigate the disease conditions if infected. Although a few vaccines have been developed with an unprecedented speed, scientists around the world are continuing pursuing the best possible vaccines with innovations. Comparing to the expensive mRNA vaccines and attenuated/inactivated SARS-CoV-2 vaccines, recombinant protein vaccines have certain advantages, including their safety (non-virus components), potential stronger immunogenicity, broader protection, ease of scaling-up production, reduced cost, etc. In this study, we reported a novel COVID-19 vaccine generated with RBD-HR1/HR2 hexamer that was creatively fused with the RBD domain and heptad repeat 1 (HR1) or heptad repeat 2 (HR2) to form a dumbbell-shaped hexamer to target the spike S1 subunit. The novel hexamer COVID-19 vaccine induced high titers of neutralizing antibody in mouse studies (>100,000), and further experiments also showed that the vaccine also induced an alternative antibody to the HR1 region, which probably alleviated the drop of immunogenicity from the frequent mutations of SARS-CoV-2.

scholarly journals Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Kazi Nabiul Alam ◽  
Md Shakib Khan ◽  
Abdur Rab Dhruba ◽  
Mohammad Monirujjaman Khan ◽  
Jehad F. Al-Amri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Confusion Matrix ◽  
Short Term ◽  
Learning Techniques ◽  
The World ◽  
Long Short Term Memory ◽  
Severe Anxiety

The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people’s feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people’s minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public’s opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.

Transcending Entrenched Tensions

2013 ◽  
Author(s):  
Margaret Lock
Keyword(s):  
Public Health ◽  
Alzheimer Disease ◽  
Paradigm Shift ◽  
Global Economy ◽  
Public Health Approach ◽  
Molecular Approach ◽  
Medical Facilities ◽  
The World ◽  
Global Concern ◽  
Aging Societies

This chapter turns to the global concern about aging societies, and the so-called epidemic of aging. It argues that a public health approach to aging and Alzheimer's will have a much greater effect in reducing the incidence of Alzheimer disease (AD) worldwide than will the technologically oriented molecular approach currently being heralded as a paradigm shift. Should such an approach be effective, and there is little evidence to date to be optimistic that this will be the case, the extent of investment in advanced medical facilities and highly trained expertise required to put it in place is not realistic beyond wealthy segments of the world, especially given the global economy of the present and the increasing gaps between rich and poor.

scholarly journals Detection of Disease in Maize Plant Using Deep Learning

2021 ◽  
Vol 36 (2) ◽  
pp. 82-88
Author(s):  
Dr.B. Rama Subba Reddy ◽  
Dr.G. Bindu Madhavi ◽  
C.H. Sri Lakshmi ◽  
Dr.K. Venkata Nagendra ◽  
Dr.R. Sridevi
Keyword(s):  
Deep Learning ◽  
Food Production ◽  
Automated Detection ◽  
Plant Diseases ◽  
Maize Plant ◽  
Small Scale ◽  
Traditional Farming ◽  
Cercospora Leaf Spot ◽  
Detection Model ◽  
Learning Techniques

Agriculture is vital to the Indian economy as over 17 percent of total GDP and employs more than 60 percent of the population relies on agriculture. This research focuses on plant diseases as they create a major threat to food production as well as for small-scale farmer’s livelihood. Expert workers are employed in traditional farming to visually evaluate row by row to identify plant diseases, which is a time-consuming, labor-intensive activity that is potentially error-prone because it is done by humans. The aim of this research is to develop an automated detection model that uses a combination of image processing and deep learning techniques (Faster R-CNN+ResNet50) to analyze real-time images and detect and classify the three common maize plant diseases: Common Rust, Cercospora Leaf Spot, and Northern Leaf Blight. The proposed system achieved 91% accuracy and successfully detects three maize diseases.

scholarly journals Existential Methods on Diabetes Detection using Machine Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 3034-3039
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Logistic Regression ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Literature Review ◽  
Learning Techniques ◽  
The World ◽  
Diabetes Prediction

Nowadays, a lot of research is going on in healthcare. One of the significant diseases increased all over the world is Diabetes Mellitus (DM). In this paper, the literature review is done on diabetes prediction using Machine Learning and Deep Learning techniques. Various ML algorithms are used using PIDD (Pima Indian diabetes dataset), and improved k- means using logistic regression among all algorithms achieved the highest accuracy. DL algorithms like CNN and LMST used in diabetic retinopathy images.

scholarly journals Deep Learning Driven Automated Detection of COVID-19 from Radiography Images: A Comparative Analysis

2020 ◽  
Author(s):  
Sejuti Rahman ◽  
Sujan Sarker ◽  
Abdullah Al Miraj ◽  
Ragib Amin Nihal ◽  
A. K. M. Nadimul Haque ◽  
...  
Keyword(s):  
Deep Learning ◽  
World Economy ◽  
Diagnostic Technique ◽  
High Accuracy ◽  
Automated Detection ◽  
Svm Classifier ◽  
X Ray ◽  
Specimen Collection ◽  
The World ◽  
Global Catastrophe

The ravage of COVID-19 is not merely limited to taking its toll with half a million fatalities. It has halted the world economy, disrupting normalcy of lives with supervening severity than any other global catastrophe of the last few decades. The majority of the vaccine discovery attempts are still on trial, making early detection and containment the only feasible redress. The existing diagnostic technique with high accuracy has the setbacks of being expensive and sophisticated, requiring skilled individuals for specimen collection and screening resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captures the researchers' interest. This survey marks a detailed inspection of the deep-learning-based automated detection of COVID-19 works done to date, methodical challenges along with probable solutions, and scopes of future exploration in this arena. We also provided a comparative quantitative analysis of the performance of 315 deep models in diagnosing COVID-19, Normal, and Pneumonia from x-ray images. Our results show that Densenet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16\%, sensitivity: 98.93\%, specificity: 98.77\%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

scholarly journals DIAG a Diagnostic Web Application Based on Lung CT Scan Images and Deep Learning

2021 ◽  
Author(s):  
Amel Imene Hadj Bouzid ◽  
Said Yahiaoui ◽  
Anis Lounis ◽  
Sid-Ahmed Berrani ◽  
Hacène Belbachir ◽  
...  
Keyword(s):  
Deep Learning ◽  
Web Application ◽  
Patient Management ◽  
Image Interpretation ◽  
Ct Scans ◽  
Diagnostic Tools ◽  
The World ◽  
Aided Diagnosis ◽  
Lung Ct ◽  
Lung Ct Scan

Coronavirus disease is a pandemic that has infected millions of people around the world. Lung CT-scans are effective diagnostic tools, but radiologists can quickly become overwhelmed by the flow of infected patients. Therefore, automated image interpretation needs to be achieved. Deep learning (DL) can support critical medical tasks including diagnostics, and DL algorithms have successfully been applied to the classification and detection of many diseases. This work aims to use deep learning methods that can classify patients between Covid-19 positive and healthy patient. We collected 4 available datasets, and tested our convolutional neural networks (CNNs) on different distributions to investigate the generalizability of our models. In order to clearly explain the predictions, Grad-CAM and Fast-CAM visualization methods were used. Our approach reaches more than 92% accuracy on 2 different distributions. In addition, we propose a computer aided diagnosis web application for Covid-19 diagnosis. The results suggest that our proposed deep learning tool can be integrated to the Covid-19 detection process and be useful for a rapid patient management.

scholarly journals Global Impacts and Nigeria Responsiveness to the COVID-19 Pandemic

2020 ◽  
pp. 27-45
Author(s):  
Isaiah Nnanna Ibeh ◽  
Seyi Samson Enitan ◽  
Richard Yomi Akele ◽  
Christy Chinwe Isitua ◽  
Felix Omorodion
Keyword(s):  
Public Health ◽  
Global Economy ◽  
Health Workers ◽  
Public Health Emergency ◽  
African Countries ◽  
The World ◽  
Global Impacts ◽  
The Government ◽  
Sporting Activities ◽  
Medical Wastes

The Coronavirus Disease – 2019 (COVID-19) is officially now a pandemic and not just a public health emergency of international concern as previously labelled. Worldwide, the new coronavirus has infected more than 4.9 million people and leaving more than 300,000 people dead in 188 countries. As countries of the world get locked down in an effort to contain the widespread of the virus, experts are concern about the global impacts of the pandemic on individuals, countries and the world at large. Millions of people are currently under quarantine across the globe. Many countries have responded by proclaiming a public health emergency, closed their borders and restrict incoming flights from high risk countries. This has grossly affected the travel plan of many. Several international programs, conferences, workshops and sporting activities are either postponed or cancelled. As the number of confirmed cases continues to escalate across the globe, hospitals seems to be running out of medical supplies, hospital spaces and personnel. Health workers are being overwhelmed by the numbers of people requesting for testing and treatment. Many of such health workers have been infected with the coronavirus and even lost their lives since the fight against COVID-19 started. Public health experts are also concerned about the huge medical wastes coming from the hospitals at this time and the adverse effects associated with improper management of such medical wastes, both at the hospital and community levels. The pandemic has also impacted negatively on the global economy. There have been serious crises in the stock market, with gross fall in the price of crude oil resulting in inflation and economic hardship among the populace. Many are currently out of job and as a result, the level of crime, protest and violence have continued to escalate in different parts of the world. The deaths of loved ones due to the coronavirus has left many emotionally traumatized. Nigeria, like other African countries is not spared of the ravaging effects of the pandemic, even as the government take strict measures to contain the virus. No doubt, this is very challenging, but the country is capable of surmounting the virus with the needed help from her international partners and cooperation from the citizenry. But if we as a people, remain complacent and continue with business as usual, without taking measures to flatten the curve, the disease will escalate too quickly beyond our capacity to handle and our health system will be overwhelmed and may collapse eventually. We cannot therefore afford to be complacent in our response to containing the pandemic.

scholarly journals Diagnosis of COVID-19 using CT scan images and deep learning techniques

2020 ◽  
Author(s):  
Vruddhi Shah ◽  
Rinkal Keniya ◽  
Akanksha Shridharani ◽  
Manav Punjabi ◽  
Jainam Shah ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Ct Scan ◽  
Efficient Method ◽  
Learning Models ◽  
Automated Diagnosis ◽  
Learning Techniques ◽  
The World

Early diagnosis of the coronavirus disease in 2019 (COVID-19) is essential for controlling this pandemic. COVID-19 has been spreading rapidly all over the world. There is no vaccine available for this virus yet. Fast and accurate COVID-19 screening is possible using computed tomography (CT) scan images. The deep learning techniques used in the proposed method was based on a convolutional neural network (CNN). Our manuscript focuses on differentiating the CT scan images of COVID-19 and non-COVID 19 CT using different deep learning techniques. A self developed model named CTnet-10 was designed for the COVID-19 diagnosis, having an accuracy of 82.1 %. Also, other models that we tested are DenseNet-169, VGG-16, ResNet-50, InceptionV3, and VGG-19. The VGG-19 proved to be superior with an accuracy of 94.52 % as compared to all other deep learning models. Automated diagnosis of COVID-19 from the CT scan pictures can be used by the doctors as a quick and efficient method for COVID-19 screening.

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