scholarly journals A Deep Learning Based Approach for Patient Pulmonary CT Image Screening to Predict Coronavirus (SARS-CoV-2) Infection

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1735
Author(s):  
Parag Verma ◽  
Ankur Dumka ◽  
Rajesh Singh ◽  
Alaknanda Ashok ◽  
Aman Singh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Influenza A ◽  
Human Life ◽  
Ct Images ◽  
Learning Technology ◽  
Ct Image ◽  
The Novel ◽  
Early Screening ◽  
The World ◽  
Novel Coronavirus

The novel coronavirus (nCoV-2019) is responsible for the acute respiratory disease in humans known as COVID-19. This infection was found in the Wuhan and Hubei provinces of China in the month of December 2019, after which it spread all over the world. By March, 2020, this epidemic had spread to about 117 countries and its different variants continue to disturb human life all over the world, causing great damage to the economy. Through this paper, we have attempted to identify and predict the novel coronavirus from influenza-A viral cases and healthy patients without infection through applying deep learning technology over patient pulmonary computed tomography (CT) images, as well as by the model that has been evaluated. The CT image data used under this method has been collected from various radiopedia data from online sources with a total of 548 CT images, of which 232 are from 12 patients infected with COVID-19, 186 from 17 patients with influenza A virus, and 130 are from 15 healthy candidates without infection. From the results of examination of the reference data determined from the point of view of CT imaging cases in general, the accuracy of the proposed model is 79.39%. Thus, this deep learning model will help in establishing early screening of COVID-19 patients and thus prove to be an analytically robust method for clinical experts.

scholarly journals Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation

2020 ◽  
Author(s):  
A. Amyar ◽  
R. Modzelewski ◽  
S. Ruan
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Ct Imaging ◽  
Chest Ct ◽  
The Novel ◽  
Imaging Analysis ◽  
Proposed Model ◽  
Fast Spreading ◽  
Novel Coronavirus ◽  
Deep Learning Model

ABSTRACTThe fast spreading of the novel coronavirus COVID-19 has aroused worldwide interest and concern, and caused more than one million and a half confirmed cases to date. To combat this spread, medical imaging such as computed tomography (CT) images can be used for diagnostic. An automatic detection tools is necessary for helping screening COVID-19 pneumonia using chest CT imaging. In this work, we propose a multitask deep learning model to jointly identify COVID-19 patient and segment COVID-19 lesion from chest CT images. Our motivation is to leverage useful information contained in multiple related tasks to help improve both segmentation and classification performances. Our architecture is composed by an encoder and two decoders for reconstruction and segmentation, and a multi-layer perceptron for classification. The proposed model is evaluated and compared with other image segmentation and classification techniques using a dataset of 1044 patients including 449 patients with COVID-19, 100 normal ones, 98 with lung cancer and 397 of different kinds of pathology. The obtained results show very encouraging performance of our method with a dice coefficient higher than 0.78 for the segmentation and an area under the ROC curve higher than 93% for the classification.

scholarly journals Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models

2021 ◽  
Vol 11 (15) ◽  
pp. 7004
Author(s):  
Shreya Biswas ◽  
Somnath Chatterjee ◽  
Arindam Majee ◽  
Shibaprasad Sen ◽  
Friedhelm Schwenker ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Respiratory Diseases ◽  
Ct Images ◽  
Chest Ct ◽  
The Novel ◽  
Proposed Model ◽  
The World ◽  
Prediction Capability ◽  
Efficient Transfer

The novel SaRS-CoV-2 virus, responsible for the dangerous pneumonia-type disease, COVID-19, has undoubtedly changed the world by killing at least 3,900,000 people as of June 2021 and compromising the health of millions across the globe. Though the vaccination process has started, in developing countries such as India, the process has not been fully developed. Thereby, a diagnosis of COVID-19 can restrict its spreading and level the pestilence curve. As the quickest indicative choice, a computerized identification framework ought to be carried out to hinder COVID-19 from spreading more. Meanwhile, Computed Tomography (CT) imaging reveals that the attributes of these images for COVID-19 infected patients vary from healthy patients with or without other respiratory diseases, such as pneumonia. This study aims to establish an effective COVID-19 prediction model through chest CT images using efficient transfer learning (TL) models. Initially, we used three standard deep learning (DL) models, namely, VGG-16, ResNet50, and Xception, for the prediction of COVID-19. After that, we proposed a mechanism to combine the above-mentioned pre-trained models for the overall improvement of the prediction capability of the system. The proposed model provides 98.79% classification accuracy and a high F1-score of 0.99 on the publicly available SaRS-CoV-2 CT dataset. The model proposed in this study is effective for the accurate screening of COVID-19 CT scans and, hence, can be a promising supplementary diagnostic tool for the forefront clinical specialists.

scholarly journals Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images

2020 ◽  
Author(s):  
Song Ying ◽  
Shuangjia Zheng ◽  
Liang Li ◽  
Xiang Zhang ◽  
Xiaodong Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Chest Ct ◽  
Accurate Diagnosis ◽  
Ct Diagnosis ◽  
Accurate Identification ◽  
The World ◽  
Novel Coronavirus ◽  
Healthy Persons

BackgroundA novel coronavirus (COVID-19) has emerged recently as an acute respiratory syndrome. The outbreak was originally reported in Wuhan, China, but has subsequently been spread world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images.Materials and MethodsWe collected chest CT scans of 88 patients diagnosed with the COVID-19 from hospitals of two provinces in China, 101 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the collected dataset, a deep learning-based CT diagnosis system (DeepPneumonia) was developed to identify patients with COVID-19.ResultsThe experimental results showed that our model can accurately identify the COVID-19 patients from others with an excellent AUC of 0.99 and recall (sensitivity) of 0.93. In addition, our model was capable of discriminating the COVID-19 infected patients and bacteria pneumonia-infected patients with an AUC of 0.95, recall (sensitivity) of 0.96. Moreover, our model could localize the main lesion features, especially the ground-glass opacity (GGO) that is of great help to assist doctors in diagnosis. The diagnosis for a patient could be finished in 30 seconds, and the implementation on Tianhe-2 supercompueter enables a parallel executions of thousands of tasks simultaneously. An online server is available for online diagnoses with CT images by http://biomed.nscc-gz.cn/server/Ncov2019.ConclusionsThe established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.

scholarly journals COVID-19 : Detailed Analytics & Predictive Modelling using Deep Learning

2020 ◽  
pp. 95-104
Author(s):  
Arjun Dutta ◽  
Aman Gupta ◽  
Farhan Hai Khan
Keyword(s):  
Deep Learning ◽  
Predictive Modelling ◽  
Mortality Rates ◽  
Infection Frequency ◽  
The Novel ◽  
Current Spread ◽  
The World ◽  
Novel Coronavirus ◽  
Death Cases ◽  
Future Growth

The rise of COVID-19 pandemic is alarmingly exponential in nature, and there is no certain predictability in the patterns of its future growth. This research aims at providing suitable insights into the current spread of the novel coronavirus (COVID 19) and conveys detailed analysis into current statistical growth rates of the infection frequency for diverse cases. We also provide schematic visualizations contrast and compare trends between various countries and India. We inspect closely and differentiate between the curves for the mortality rates globally. Also projected categorically most affected states of India.Finally, this research proposes methodologies for design and implementation of deep learning models for the predictive modelling of the various cases around the world and delivers predictions till March 2021 for confirmed and death cases.

Comparative docking of SARS-CoV-2 receptors antagonists from repurposing drugs

2020 ◽  
Author(s):  
Micael Davi Lima de Oliveira ◽  
Kelson Mota Teixeira de Oliveira
Keyword(s):  
World Health Organisation ◽  
World Health ◽  
Lung Cells ◽  
The Novel ◽  
High Modulation ◽  
Main Protease ◽  
The World ◽  
Novel Coronavirus ◽  
Viral Receptors ◽  
Theoretical Results

According to the World Health Organisation, until 16 June, 2020, the number of confirmed and notified cases of COVID-19 has already exceeded 7.9 million with approximately 434 thousand deaths worldwide. This research aimed to find repurposing antagonists, that may inhibit the activity of the main protease (Mpro) of the SARS-CoV-2 virus, as well as partially modulate the ACE2 receptors largely found in lung cells, and reduce viral replication by inhibiting Nsp12 RNA polymerase. Docking molecular simulations were performed among a total of 60 structures, most of all, published in the literature against the novel coronavirus. The theoretical results indicated that, in comparative terms, paritaprevir, ivermectin, ledipasvir, and simeprevir, are among the most theoretical promising drugs in remission of symptoms from the disease. Furthermore, also corroborate indinavir to the high modulation in viral receptors. The second group of promising drugs includes remdesivir and azithromycin. The repurposing drugs HCQ and chloroquine were not effective in comparative terms to other drugs, as monotherapies, against SARS-CoV-2 infection.

Association and treatment of diabetes in patients affected by Covid-19

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 1198-1201
Author(s):  
Syed Yasir Afaque
Keyword(s):  
Diabetes Mellitus ◽  
Prognostic Value ◽  
Heart Diseases ◽  
The Novel ◽  
The World ◽  
Treatment Of Diabetes ◽  
Coronavirus Infection ◽  
Composite Outcomes ◽  
Novel Coronavirus ◽  
The Impact

In December 2019, a unique coronavirus infection, SARS-CoV-2, was first identified in the province of Wuhan in China. Since then, it spread rapidly all over the world and has been responsible for a large number of morbidity and mortality among humans. According to a latest study, Diabetes mellitus, heart diseases, Hypertension etc. are being considered important risk factors for the development of this infection and is also associated with unfavorable outcomes in these patients. There is little evidence concerning the trail back of these patients possibly because of a small number of participants and people who experienced primary composite outcomes (such as admission in the ICU, usage of machine-driven ventilation or even fatality of these patients). Until now, there are no academic findings that have proven independent prognostic value of diabetes on death in the novel Coronavirus patients. However, there are several conjectures linking Diabetes with the impact as well as progression of COVID-19 in these patients. The aim of this review is to acknowledge about the association amongst Diabetes and the novel Coronavirus and the result of the infection in such patients.

Synthetic and semi-synthetic drugs as promising therapeutic option for the treatment of COVID-19

2020 ◽  
Vol 20 ◽  
Author(s):  
Ekta Shirbhate ◽  
Preeti Patel ◽  
Vijay K Patel ◽  
Ravichandran Veerasamy ◽  
Prabodh C Sharma ◽  
...  
Keyword(s):  
Therapeutic Option ◽  
Antiviral Treatment ◽  
The Novel ◽  
Clinical Experiences ◽  
Synthetic Compounds ◽  
Synthetic Drugs ◽  
Global Pandemic ◽  
The World ◽  
Novel Coronavirus

: The novel coronavirus disease-19 (COVID-19), a global pandemic that emerged from Wuhan, China has today travelled all around the world, so far 216 countries or territories with 21,732,472 people infected and 770,866 deaths globally (as per WHO COVID-19 update dated August 18, 2020). Continuous efforts are being made to repurpose the existing drugs and develop vaccines for combating this infection. Despite, to date, no certified antiviral treatment or vaccine prevails. Although, few candidates have displayed their efficacy in in vitro studies and are being repurposed for COVID-19 treatment. This article summarizes synthetic and semi-synthetic compounds displaying potent activity in their clinical experiences or studies against COVID-19 and also focuses on mode of action of drugs being repositioned against COVID-19.

COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

2020 ◽  
Author(s):  
Jinseok Lee
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Detection Accuracy ◽  
Ct Image ◽  
Test Dataset ◽  
Learning Framework ◽  
Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

scholarly journals An outline of SARS-CoV-2 pathogenesis and the complement cascade of immune system

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Padmalochan Hembram
Keyword(s):  
Immune System ◽  
Dark Side ◽  
Main Body ◽  
The Novel ◽  
Transmission Chain ◽  
The World ◽  
Therapeutic Molecules ◽  
Rapid Transmission ◽  
Novel Coronavirus ◽  
Near Future

Abstract Background Coronavirus disease 19 is a viral infection caused by a novel coronavirus, SARS-CoV-2. It was first notified in Wuhan, China, is now spread into numerous part of the world. Thus, the world needs urgent support and encouragement to develop a vaccine or antiviral treatments to combat the atrocious outbreak. Main body of the abstract The origin of this virus is yet unknown; however, rapid transmission from human-to-human “Anthroponosis” has widely confirmed. The world is witnessing a continuous hike in SARS-CoV-2 infection. In light of the outbreak of coronavirus disease 19, we have aimed to highlight the basic and vital information about the novel coronavirus. We provide an overview of SARS-CoV-2 transmission, timeline and its pathophysiological properties which would be an aid for the development of therapeutic molecules and antiviral drugs. Immune system plays a crucial role in virus infection in order to control but may have dark side when becomes uncontrollable. The host and SARS-CoV-2 interaction describe how the virus exploits host machinery and how overactive host immune response can cause disease severity also addressed in this review. Short conclusion Safe and effective vaccines may be the game-changing tools, but in the near future wearing mask, washing hands at regular intervals, avoiding crowed, maintaining physical distancing and hygienic surrounding, must be good practices to reduce and break the transmission chain. Still, research is ongoing not only on how vaccines protect against disease, but also against infection and transmission.

scholarly journals COVID-19 Vaccination: From Interesting Agent to the Patient

Vaccines ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 120
Author(s):  
Anis Daou
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Gastrointestinal Tract ◽  
Circulatory System ◽  
The Novel ◽  
Lead Compound ◽  
Fda Approval ◽  
The World ◽  
The Central Nervous System ◽  
Novel Coronavirus

The vaccination for the novel Coronavirus (COVID-19) is undergoing its final stages of analysis and testing. It is an impressive feat under the circumstances that we are on the verge of a potential breakthrough vaccination. This will help reduce the stress for millions of people around the globe, helping to restore worldwide normalcy. In this review, the analysis looks into how the new branch of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) came into the forefront of the world like a pandemic. This review will break down the details of what COVID-19 is, the viral family it belongs to and its background of how this family of viruses alters bodily functions by attacking vital human respiratory organs, the circulatory system, the central nervous system and the gastrointestinal tract. This review also looks at the process a new drug analogue undergoes, from (i) being a promising lead compound to (ii) being released into the market, from the drug development and discovery stage right through to FDA approval and aftermarket research. This review also addresses viable reasoning as to why the SARS-CoV-2 vaccine may have taken much less time than normal in order for it to be released for use.

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