• español
    • English
    • português
  • English 
    • español
    • English
    • português
  • Login
View Item 
  •   Home
  • Productos de Investigación - Creación
  • Repositorio Documental COVID-19
  • Documentos científicos relacionados a la COVID-19
  • View Item
  •   Home
  • Productos de Investigación - Creación
  • Repositorio Documental COVID-19
  • Documentos científicos relacionados a la COVID-19
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.
RecursosRecursos de apoyo¿Cómo publicar?

Browse

All of ExpeditioCommunities & CollectionsBy Issue DateAuthorsTitlesSubjects
This CollectionBy Issue DateAuthorsTitlesSubjects

My Account

LoginRegister

Statistics

View Usage StatisticsView Google Analytics Statistics
Estadísticas GTMVer Estadísticas GTM

Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks

Thumbnail

Citación

       
Export: <XML METS>
View/Open
Ver portada (186.6Kb)
Fin embargo: 
Artículo reservado (2.882Mb)
Fin embargo: 
Date
2020
Author
Toraman, Suat
Burak Alakus, Talha
Turkoglu, Ibrahim
Metadata
Show full item record
Documentos PDF
Imagenes y Videos
Captura.PNG

Abstract
Coronavirus is an epidemic that spreads very quickly. For this reason, it has very devastating effects in many areas worldwide. It is vital to detect COVID-19 diseases as quickly as possible to restrain the spread of the disease. The similarity of COVID-19 disease with other lung infections makes the diagnosis difficult. In addition, the high spreading rate of COVID-19 increased the need for a fast system for the diagnosis of cases. For this purpose, interest in various computer-aided (such as CNN, DNN, etc.) deep learning models has been increased. In these models, mostly radiology images are applied to determine the positive cases. Recent studies show that, radiological images contain important information in the detection of coronavirus. In this study, a novel artificial neural network, Convolutional CapsNet for the detection of COVID-19 disease is proposed by using chest X-ray images with capsule networks. The proposed approach is designed to provide fast and accurate diagnostics for COVID-19 diseases with binary classification (COVID-19, and No-Findings), and multi-class classification (COVID-19, and No-Findings, and Pneumonia). The proposed method achieved an accuracy of 97.24%, and 84.22% for binary class, and multiclass, respectively. It is thought that the proposed method may help physicians to diagnose COVID-19 disease and increase the diagnostic performance. In addition, we believe that the proposed method may be an alternative method to diagnose COVID-19 by providing fast screening
URI
http://hdl.handle.net/20.500.12010/11420
Link to resource
https://doi.org/10.1016/j.chaos.2020.110122
Collections
  • Documentos científicos relacionados a la COVID-19 [2292]
Estadísticas Google Analytics
Comments

Respuesta Comentario Repositorio Expeditio

Gracias por tomarse el tiempo para darnos su opinión.


Carrera 4 # 22-61 Teléfono: (+57 1) 242 7030 - 018000111022 Fax: (+57 1) 561 2107 Bogotá D.C., Colombia

Fundación Universitaria de Bogotá Jorge Tadeo Lozano | Vigilada Mineducación

Institución de educación superior privada, de utilidad común, sin ánimo de lucro y su carácter académico es el de Universidad.

Reconocimiento personería jurídica: Resolución 2613 del 14 de agosto de 1959 Minjusticia.

Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional.

 

Términos y condiciones | Políticas

 

 


Carrera 4 # 22-61 Teléfono: (+57 1) 242 7030 - 018000111022 Fax: (+57 1) 561 2107 Bogotá D.C., Colombia

Fundación Universitaria de Bogotá Jorge Tadeo Lozano | Vigilada Mineducación

Institución de educación superior privada, de utilidad común, sin ánimo de lucro y su carácter académico es el de Universidad.

Reconocimiento personería jurídica: Resolución 2613 del 14 de agosto de 1959 Minjusticia.

Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional.

 

Términos y condiciones | Políticas