Computer Vision: Healthcare Applications to Tackle COVID-19

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 1671

Special Issue Editor


E-Mail Website
Guest Editor
School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Interests: IoT; machine learning; healthcare; pandemic; blockchain; convergence; health monitoring; networks; wireless networked control systems; the wireless sensor systems; Parkinson’s disease; wearable technology; sensors; internet of things; artificial intelligence; deep learning; remote monitoring; smart personalized healthcare

Special Issue Information

Dear Colleagues, 

The latest anxiety to public health, governments, and medical communities is the growing disease of COVID-19, which is a respiratory infection similar to classic pneumonia. Computer Vision is a subfield of Deep Learning that has recently gained popularity in dealing with different complex tasks in the healthcare area. Clinical image processing and analysis are elementary to understanding and predicting clinical images in healthcare applications. We aim to support researchers to publish their experimental and review papers in as much detail as possible. The articles should cover some practical applications of CV techniques related to COVID-19 disease and indicate some unique and original features of CV used for a healthcare application. There will be no restriction considering the length of the submitted papers.

Subjects consist of the following areas, but are not limited to:

  • COVID-19 Image Datasets for Deep Learning Models
  • Computer-Aided Diagnosis
  • Medical Image Processing and Analysis
  • Video Processing and Analysis for COVID-19 Diagnosis
  • Weakly-Supervised Medical Image Segmentation
  • Deep Learning for Medical Imaging
  • Mask Detection
  • Thermography
  • Disease Progression Score
  • Pandemic Drones
  • X-Ray Radiography

Prof. Dr. Muhammad Ali Imran
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computer vision
  • COVID-19
  • deep learning
  • machine learning
  • healthcare applications
  • classification
  • object detection
  • image segmentation

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Review

22 pages, 5121 KiB  
Review
A Review of Deep Learning Imaging Diagnostic Methods for COVID-19
by Tao Zhou, Fengzhen Liu, Huiling Lu, Caiyue Peng and Xinyu Ye
Electronics 2023, 12(5), 1167; https://doi.org/10.3390/electronics12051167 - 28 Feb 2023
Cited by 4 | Viewed by 1405
Abstract
COVID-19 (coronavirus disease 2019) is a new viral infection disease that is widely spread worldwide. Deep learning plays an important role in COVID-19 images diagnosis. This paper reviews the recent progress of deep learning in COVID-19 images applications from five aspects; Firstly, 33 [...] Read more.
COVID-19 (coronavirus disease 2019) is a new viral infection disease that is widely spread worldwide. Deep learning plays an important role in COVID-19 images diagnosis. This paper reviews the recent progress of deep learning in COVID-19 images applications from five aspects; Firstly, 33 COVID-19 datasets and data enhancement methods are introduced; Secondly, COVID-19 classification methods based on supervised learning are summarized from four aspects of VGG, ResNet, DenseNet and Lightweight Networks. The COVID-19 segmentation methods based on supervised learning are summarized from four aspects of attention mechanism, multiscale mechanism, residual connectivity mechanism, and dense connectivity mechanism; Thirdly, the application of deep learning in semi-supervised COVID-19 images diagnosis in terms of consistency regularization methods and self-training methods. Fourthly, the application of deep learning in unsupervised COVID-19 diagnosis in terms of autoencoder methods and unsupervised generative adversarial methods. Moreover, the challenges and future work of COVID-19 images diagnostic methods in the field of deep learning are summarized. This paper reviews the latest research status of COVID-19 images diagnosis in deep learning, which is of positive significance to the detection of COVID-19. Full article
(This article belongs to the Special Issue Computer Vision: Healthcare Applications to Tackle COVID-19)
Show Figures

Figure 1

Back to TopTop