scholarly journals Detection of COVID 19 from CT Image by The Novel LeNet-5 CNN Architecture

2020 ◽  
Author(s):  
Md. Rakibul Islam ◽  
Abdul Matin
Keyword(s):  
Ct Image ◽  
The Novel

scholarly journals DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image

2020 ◽  
Author(s):  
Najmul Hasan ◽  
Yukun Bao ◽  
Ashadullah Shawon
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Second World War ◽  
Ct Image ◽  
The Novel ◽  
Promising Technique ◽  
Promising Alternative ◽  
Novel Approach ◽  
Healthcare Situation ◽  
Second World

Abstract Public health and human lives recently have been impacted by the devastating effect of Coronavirus 2019. This catastrophic effect has destroyed the human experience by creating a chaotic healthcare situation infinitely more destructive than the Second World War. Strong communicable characteristics of COVID-19 among human communities make the world’s situation a severe pandemic. Due to the unavailable vaccine of COVID-19 to control rather than cure, early and accurate detection of the virus can be a promising technique for tracking and preventing the infection spreading (e.g., by isolating the patients). This situation indicates to improve auxiliary COVID-19 detection technique. Computed tomography (CT) imaging is a mostly used technique for pneumonia because of its common availability. The application of artificial intelligence systems integrated with images can be a promising alternative for the identification of COVID-19. This paper presents a promising technique of predicting COVID-19 patients from the CT image using convolutional neural networks (CNN). The novel approach is based on DenseNet is the updated CNN architecture in the present state to detect COVID-19. The results outperformed 92% accuracy, with 95% recall showing good performance for the identification of COVID-19.

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.

The novel steroidal glycoside from the dried bulb of Allium Macrostemon Bunge inhibits platelet activation

2010 ◽  
Vol 34 (8) ◽  
pp. S33-S33
Author(s):  
Wenchao Ou ◽  
Haifeng Chen ◽  
Yun Zhong ◽  
Benrong Liu ◽  
Keji Chen
Keyword(s):  
Platelet Activation ◽  
The Novel ◽  
Steroidal Glycoside ◽  
Allium Macrostemon

The Involuntary Capture of Attention by Sound

2010 ◽  
Vol 57 (1) ◽  
pp. 68-76 ◽  
Author(s):  
Fabrice B. R. Parmentier ◽  
Pilar Andrés
Keyword(s):  
Older Adults ◽  
Mismatch Negativity ◽  
Visual Task ◽  
The Novel ◽  
Auditory Oddball ◽  
Current Trial ◽  
Electrophysiological Responses ◽  
Electrophysiological Studies ◽  
The Cross ◽  
Task Sets

The presentation of auditory oddball stimuli (novels) among otherwise repeated sounds (standards) triggers a well-identified chain of electrophysiological responses: The detection of acoustic change (mismatch negativity), the involuntary orientation of attention to (P3a) and its reorientation from the novel. Behaviorally, novels reduce performance in an unrelated visual task (novelty distraction). Past studies of the cross-modal capture of attention by acoustic novelty have typically discarded from their analysis the data from the standard trials immediately following a novel, despite some evidence in mono-modal oddball tasks of distraction extending beyond the presentation of deviants/novels (postnovelty distraction). The present study measured novelty and postnovelty distraction and examined the hypothesis that both types of distraction may be underpinned by common frontally-related processes by comparing young and older adults. Our data establish that novels delayed responses not only on the current trial and but also on the subsequent standard trial. Both of these effects increased with age. We argue that both types of distraction relate to the reconfiguration of task-sets and discuss this contention in relation to recent electrophysiological studies.

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