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COVID-19: Medical Internet of Things and Big Data Analytics
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COVID-19: Medical Internet of Things and Big Data Analytics

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 9201

Special Issue Editor

Prof. Dr. Newton Howard

E-Mail Website
Guest Editor
Computational Neuroscience and Functional Neurosurgery, University of Oxford, Oxford OX3 9DU, UK
Interests: cognitive computing; machine learning; artificial intelligence; big data; biomedical devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The coronavirus pandemic, which began in January 2020, was a crisis that affected the entire world medically, socially, and economically. Most of the world was unprepared for such a catastrophe that overwhelmed the healthcare system and forced people to stay in their homes. The pandemic not only highlighted problems and unpreparedness in our current healthcare system, but also created the opportunity for telehealth and data analytics to come to the forefront.

This Special Issue invites high quality papers that discuss COVID-19 and the medical Internet of Things and big data analytics. This can include but is not limited to the topics listed below.

Prof. Dr. Newton Howard
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. Big Data and Cognitive Computing is an international peer-reviewed open access monthly 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 1800 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

  • Telehealth
  • Big Data
  • Tracking and predicting pandemics and other health crises
  • Disease prevention maps
  • Homestay data
  • Digital surveillance
  • DNA
  • Genomics
  • Flattening the curve COVID modeling
  • Healthcare supply chain
  • E-health
  • M-health
  • Telemedicine
  • Personalized medicine
  • Machine learning for testing
  • Machine learning for diagnostics
  • Machine learning
  • Impact on internet of things
  • Impact of COVID-19 on cybersecurity and hacking
  • Impact on zoom and other conference platforms

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Published Papers (1 paper)

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Research

25 pages, 11236 KiB  
Article
Explainable COVID-19 Detection on Chest X-rays Using an End-to-End Deep Convolutional Neural Network Architecture
by Mohamed Chetoui, Moulay A. Akhloufi, Bardia Yousefi and El Mostafa Bouattane
Big Data Cogn. Comput. 2021, 5(4), 73; https://doi.org/10.3390/bdcc5040073 - 7 Dec 2021
Cited by 23 | Viewed by 7637
Abstract
The coronavirus pandemic is spreading around the world. Medical imaging modalities such as radiography play an important role in the fight against COVID-19. Deep learning (DL) techniques have been able to improve medical imaging tools and help radiologists to make clinical decisions for [...] Read more.
The coronavirus pandemic is spreading around the world. Medical imaging modalities such as radiography play an important role in the fight against COVID-19. Deep learning (DL) techniques have been able to improve medical imaging tools and help radiologists to make clinical decisions for the diagnosis, monitoring and prognosis of different diseases. Computer-Aided Diagnostic (CAD) systems can improve work efficiency by precisely delineating infections in chest X-ray (CXR) images, thus facilitating subsequent quantification. CAD can also help automate the scanning process and reshape the workflow with minimal patient contact, providing the best protection for imaging technicians. The objective of this study is to develop a deep learning algorithm to detect COVID-19, pneumonia and normal cases on CXR images. We propose two classifications problems, (i) a binary classification to classify COVID-19 and normal cases and (ii) a multiclass classification for COVID-19, pneumonia and normal. Nine datasets and more than 3200 COVID-19 CXR images are used to assess the efficiency of the proposed technique. The model is trained on a subset of the National Institute of Health (NIH) dataset using swish activation, thus improving the training accuracy to detect COVID-19 and other pneumonia. The models are tested on eight merged datasets and on individual test sets in order to confirm the degree of generalization of the proposed algorithms. An explainability algorithm is also developed to visually show the location of the lung-infected areas detected by the model. Moreover, we provide a detailed analysis of the misclassified images. The obtained results achieve high performances with an Area Under Curve (AUC) of 0.97 for multi-class classification (COVID-19 vs. other pneumonia vs. normal) and 0.98 for the binary model (COVID-19 vs. normal). The average sensitivity and specificity are 0.97 and 0.98, respectively. The sensitivity of the COVID-19 class achieves 0.99. The results outperformed the comparable state-of-the-art models for the detection of COVID-19 on CXR images. The explainability model shows that our model is able to efficiently identify the signs of COVID-19. Full article
(This article belongs to the Special Issue COVID-19: Medical Internet of Things and Big Data Analytics)
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