scholarly journals A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19)

2020 ◽  
Author(s):  
Ahmed Abdullah Farid ◽  
Gamal Ibrahim Selim ◽  
Hatem Awad A. Khater
Keyword(s):  
Feature Extraction ◽  
Virus Disease ◽  
Real Data ◽  
Ct Images ◽  
Learning Tools ◽  
Corona Virus ◽  
Novel Approach ◽  
Hybrid Classification ◽  
Hybrid Feature Extraction ◽  
First Time

The paper demonstrates the analysis of Corona Virus Disease based on a probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed approach for feature extraction. The combination of the conventional statistical and machine learning tools is applied for feature extraction from CT images through four images filters in combination with proposed composite hybrid feature extraction (CHFS). The selected features were classified by the stack hybrid classification system(SHC). Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

scholarly journals A CNN Classification Model For Diagnosis Covid19

2020 ◽  
Author(s):  
Ahmed Abdullah Farid ◽  
hatem khater ◽  
gamal selim
Keyword(s):  
Neural Network ◽  
Virus Disease ◽  
Real Data ◽  
Classification Model ◽  
X Ray ◽  
Corona Virus ◽  
Chest X Ray ◽  
Neural Network Structure ◽  
Extraction Model ◽  
First Time

The paper demonstrates the analysis of Corona Virus Disease based on a CNN probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed Convolution neural network structure. The Study is validated on 2002 chest X-ray images with 60 confirmed positive covid19 cases and (650 bacterial – 412 viral -880 normal) x-ray images. The proposed CNN compared with traditional classifiers with proposed CHFS feature extraction model. The experimental study has done with real data demonstrates the feasibility and potential of the proposed approach for the said cause. The result of proposed CNN structure has been successfully done to achieve 98.20% accuracy of covid19 potential cases with comparable of traditional classifiers.

scholarly journals A Novel Approach for Efficient Forgery Image Detection Using Hybrid Feature Extraction and Classification

2018 ◽  
Vol 7 (3.27) ◽  
pp. 215
Author(s):  
G Clara Shanthi ◽  
V Cyril Raj
Keyword(s):  
Feature Extraction ◽  
Support Vector ◽  
Forgery Detection ◽  
Image Splicing ◽  
Image Forgery ◽  
Second Stage ◽  
Image Forgery Detection ◽  
Novel Approach ◽  
Different Types ◽  
Hybrid Feature Extraction

Image forgery detection is developing as one of the major research topic among researchers in the area of image forensics. These image forgery detection is addressed by two different types: (i) Active, (ii) Passive. Further consist of some different methods, such as Copy-Move, Image Splicing, and Retouching. Development of the image forgery is very necessary to detect as the image is true or it is forgery. In this paper, an efficient forgery detection and classification technique is proposed by three different stages. At first stage, preprocessing is carried out using bilateral filtering to remove noise. At second stage, extract unique features from forged image by using efficient feature extraction technique namely Gray Level Co-occurance Matrices (GLCM). Here, the GLCM improves the feature extraction accuracy. Finally, forged image is detected by classifying the type of image forgery using Multi Class- Support Vector Machine (SVM). Also, the performance of the proposed method is analyzed using the following metrics: accuracy, sensitivity and specificity.  

scholarly journals A primer on the in-home teleneurologic examination

2020 ◽  
pp. 10.1212/CPJ.0000000000000876 ◽  
Author(s):  
Christopher J. Boes ◽  
Andrea N. Leep Hunderfund ◽  
Jennifer M. Martinez-Thompson ◽  
Neeraj Kumar ◽  
Rodolfo Savica ◽  
...  
Keyword(s):  
Virus Disease ◽  
Medical Professional ◽  
Clinical Question ◽  
Face To Face ◽  
Corona Virus ◽  
First Time

It is imperative in the corona virus disease 2019 (COVID-19) pandemic that we serve our patients by implementing teleneurology visits for those who require neurologic advice but do not need to be seen face-to-face. The authors propose a thorough, practical, in-home, teleneurologic examination that can be completed without the assistance of an on-the-scene medical professional, and can be tailored to the clinical question. We hope to assist trainees and practicing neurologists doing patient video visits for the first time during the COVID-19 pandemic, focusing on what can, rather than what cannot, be easily examined.

Improved FCM Based on Gaussian Kernel and Crow Search Optimization for ROI Extraction on Corona Virus Disease (COVID-19) CT Images

2021 ◽  
pp. 173-192
Author(s):  
S. N. Kumar ◽  
A. Lenin Fred ◽  
L. R. Jonisha Miriam ◽  
H. Ajay Kumar ◽  
Parasuraman Padmanabhan ◽  
...  
Keyword(s):  
Virus Disease ◽  
Ct Images ◽  
Gaussian Kernel ◽  
Corona Virus ◽  
Search Optimization ◽  
Roi Extraction

scholarly journals A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)

2021 ◽  
Author(s):  
Shuai Wang ◽  
Bo Kang ◽  
Jinlu Ma ◽  
Xianjun Zeng ◽  
Mingming Xiao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Virus Disease ◽  
Ct Images ◽  
Viral Pneumonia ◽  
Diagnostic Methods ◽  
Deep Learning Algorithm ◽  
Corona Virus ◽  
Total Accuracy

Abstract Objective The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. Results The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. Conclusion These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Key Points • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.

Indikationen für chinesische Arzneimitteltherapie bei Covid-19-Erkrankungen

2020 ◽  
Vol 12 (03) ◽  
pp. 18-18
Author(s):  
Christian Thede
Keyword(s):  
Virus Disease ◽  
Traditionelle Chinesische Medizin ◽  
Chinesische Medizin ◽  
Corona Virus ◽  
Chinesische Arzneimitteltherapie

SummaryIn Reaktion auf den massiven Ausbruch von Covid-19-Erkrankungen in der Region Wuhan wurde von staatlicher Seite bereits Ende Januar 2020 eine Expertenkommission namhafter chinesischer TCM-Fachleute berufen. Nach der Sichtung einer größeren Anzahl von Patienten in Wuhan wurdenTherapieprotokolle für verschiedene Krankheitsstadien formuliert, die in den „Guidance for Corona Virus Disease 2019“ des Generalbüros der Nationalen Hygiene und Gesundheitskommission und des Büros der staatlichen Verwaltung für traditionelle chinesische Medizin aufgenommen wurden.

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