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Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting...
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Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 December 2021) | Viewed by 93508

Special Issue Editors

Prof. Dr. Anton Civit

E-Mail Website
Guest Editor
Robotics and Computer Technology Lab, University of Seville, 41012 Seville, Spain
Interests: deep learning; e-Health; computer architecture; neuromorphic engineering; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Manuel Dominguez-Morales

E-Mail Website
Guest Editor
Robotics and Computer Technology Lab, University of Seville, Seville, Spain
Interests: deep learning; computer vision; e-Health; embedded systems; neuromorphic engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The current pandemic situation caused by the COVID-19 outbreak has plunged humanity into a continuous state of fear, the outcome of which is not yet known.

Among the reasons that have caused this spread are the lack of foresight, the lack of sanitary measures, and the disinformation.

The scientific community must play a leading role at this time, and it is our obligation to step forward to assist in the eradication of this virus.

For this purpose, it is important to find mechanisms, techniques and/or systems capable of helping during the various stages of this pandemic: evolution, early diagnosis, prevention of spread, isolation measures, etc.

The main objective of this Special Issue is to unify the work done for this purpose by the multiple scientific branches that are currently working to help in this task.

We will overcome this crisis together without any doubt.

Prof. Dr. Anton Civit
Dr. Manuel Dominguez-Morales
Guest Editors

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. Applied Sciences 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

  • Statistical studies of evolution and future trends of COVID-19
  • Machine learning techniques for diagnosis COVID-19
  • Medical images analysis for diagnosis COVID-19
  • Experimental studies about COVID-19 characteristics
  • Wearable systems to prevent the contagion of COVID-19
  • Apps for tracking and prevention of COVID-19
  • Systems, mechanisms, and techniques to avoid the spread of COVID-19

Published Papers (20 papers)

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Editorial

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3 pages, 172 KiB  
Editorial
Special Issue “Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis”
by Manuel Domínguez-Morales and Antón Civit
Appl. Sci. 2023, 13(1), 467; https://doi.org/10.3390/app13010467 - 29 Dec 2022
Viewed by 753
Abstract
Since its emergence at the end of 2019, the pandemic caused by the COVID-19 virus has led to multiple changes in health protocols around the world [...] Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)

Research

Jump to: Editorial, Other

12 pages, 883 KiB  
Article
Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models
by Nader Essam, Abdullah M. Moussa, Khaled M. Elsayed, Sherif Abdou, Mohsen Rashwan, Shaheen Khatoon, Md. Maruf Hasan, Amna Asif and Majed A. Alshamari
Appl. Sci. 2021, 11(23), 11328; https://doi.org/10.3390/app112311328 - 30 Nov 2021
Cited by 8 | Viewed by 2425
Abstract
The recent surge of social media networks has provided a channel to gather and publish vital medical and health information. The focal role of these networks has become more prominent in periods of crisis, such as the recent pandemic of COVID-19. These social [...] Read more.
The recent surge of social media networks has provided a channel to gather and publish vital medical and health information. The focal role of these networks has become more prominent in periods of crisis, such as the recent pandemic of COVID-19. These social networks have been the leading platform for broadcasting health news updates, precaution instructions, and governmental procedures. They also provide an effective means for gathering public opinion and tracking breaking events and stories. To achieve location-based analysis for social media input, the location information of the users must be captured. Most of the time, this information is either missing or hidden. For some languages, such as Arabic, the users’ location can be predicted from their dialects. The Arabic language has many local dialects for most Arab countries. Natural Language Processing (NLP) techniques have provided several approaches for dialect identification. The recent advanced language models using contextual-based word representations in the continuous domain, such as BERT models, have provided significant improvement for many NLP applications. In this work, we present our efforts to use BERT-based models to improve the dialect identification of Arabic text. We show the results of the developed models to recognize the source of the Arabic country, or the Arabic region, from Twitter data. Our results show 3.4% absolute enhancement in dialect identification accuracy on the regional level over the state-of-the-art result. When we excluded the Modern Standard Arabic (MSA) set, which is formal Arabic language, we achieved 3% absolute gain in accuracy between the three major Arabic dialects over the state-of-the-art level. Finally, we applied the developed models on a recently collected resource for COVID-19 Arabic tweets to recognize the source country from the users’ tweets. We achieved a weighted average accuracy of 97.36%, which proposes a tool to be used by policymakers to support country-level disaster-related activities. Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)
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18 pages, 2862 KiB  
Article
Machine Learning Models of COVID-19 Cases in the United States: A Study of Initial Lockdown and Reopen Regimes
by Arnold Kamis, Yudan Ding, Zhenzhen Qu and Chenchen Zhang
Appl. Sci. 2021, 11(23), 11227; https://doi.org/10.3390/app112311227 - 26 Nov 2021
Cited by 2 | Viewed by 2259
Abstract
The purpose of this paper is to model the cases of COVID-19 in the United States from 13 March 2020 to 31 May 2020. Our novel contribution is that we have obtained highly accurate models focused on two different regimes, lockdown and reopen, [...] Read more.
The purpose of this paper is to model the cases of COVID-19 in the United States from 13 March 2020 to 31 May 2020. Our novel contribution is that we have obtained highly accurate models focused on two different regimes, lockdown and reopen, modeling each regime separately. The predictor variables include aggregated individual movement as well as state population density, health rank, climate temperature, and political color. We apply a variety of machine learning methods to each regime: Multiple Regression, Ridge Regression, Elastic Net Regression, Generalized Additive Model, Gradient Boosted Machine, Regression Tree, Neural Network, and Random Forest. We discover that Gradient Boosted Machines are the most accurate in both regimes. The best models achieve a variance explained of 95.2% in the lockdown regime and 99.2% in the reopen regime. We describe the influence of the predictor variables as they change from regime to regime. Notably, we identify individual person movement, as tracked by GPS data, to be an important predictor variable. We conclude that government lockdowns are an extremely important de-densification strategy. Implications and questions for future research are discussed. Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)
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15 pages, 3151 KiB  
Article
Predicting High-Risk Groups for COVID-19 Anxiety Using AdaBoost and Nomogram: Findings from Nationwide Survey in South Korea
by Haewon Byeon
Appl. Sci. 2021, 11(21), 9865; https://doi.org/10.3390/app11219865 - 22 Oct 2021
Cited by 5 | Viewed by 1795
Abstract
People living in local communities have become more worried about infection due to the extended pandemic situation and the global resurgence of COVID-19. In this study, the author (1) selected features to be included in the nomogram using AdaBoost, which had an advantage [...] Read more.
People living in local communities have become more worried about infection due to the extended pandemic situation and the global resurgence of COVID-19. In this study, the author (1) selected features to be included in the nomogram using AdaBoost, which had an advantage in increasing the classification accuracy of single learners and (2) developed a nomogram for predicting high-risk groups of coronavirus anxiety while considering both prediction performance and interpretability based on this. Among 210,606 adults (95,287 males and 115,319 females) in South Korea, 39,768 people (18.9%) experienced anxiety due to COVID-19. The AdaBoost model confirmed that education level, awareness of neighbors/colleagues’ COVID-19 response, age, gender, and subjective stress were five key variables with high weight in predicting anxiety induced by COVID-19 for adults living in South Korean communities. The developed logistic regression nomogram predicted that the risk of anxiety due to COVID-19 would be 63% for a female older adult who felt a lot of subjective stress, did not attend a middle school, was 70.6 years old, and thought that neighbors and colleagues responded to COVID-19 appropriately (classification accuracy = 0.812, precision = 0.761, recall = 0.812, AUC = 0.688, and F-1 score = 0.740). Prospective or retrospective cohort studies are required to causally identify the characteristics of anxiety disorders targeting high-risk COVID-19 anxiety groups identified in this study. Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)
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24 pages, 22443 KiB  
Article
A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection
by Najam-ur Rehman, Muhammad Sultan Zia, Talha Meraj, Hafiz Tayyab Rauf, Robertas Damaševičius, Ahmed M. El-Sherbeeny and Mohammed A. El-Meligy
Appl. Sci. 2021, 11(19), 9023; https://doi.org/10.3390/app11199023 - 28 Sep 2021
Cited by 45 | Viewed by 2822
Abstract
Chest diseases can be dangerous and deadly. They include many chest infections such as pneumonia, asthma, edema, and, lately, COVID-19. COVID-19 has many similar symptoms compared to pneumonia, such as breathing hardness and chest burden. However, it is a challenging task to differentiate [...] Read more.
Chest diseases can be dangerous and deadly. They include many chest infections such as pneumonia, asthma, edema, and, lately, COVID-19. COVID-19 has many similar symptoms compared to pneumonia, such as breathing hardness and chest burden. However, it is a challenging task to differentiate COVID-19 from other chest diseases. Several related studies proposed a computer-aided COVID-19 detection system for the single-class COVID-19 detection, which may be misleading due to similar symptoms of other chest diseases. This paper proposes a framework for the detection of 15 types of chest diseases, including the COVID-19 disease, via a chest X-ray modality. Two-way classification is performed in proposed Framework. First, a deep learning-based convolutional neural network (CNN) architecture with a soft-max classifier is proposed. Second, transfer learning is applied using fully-connected layer of proposed CNN that extracted deep features. The deep features are fed to the classical Machine Learning (ML) classification methods. However, the proposed framework improves the accuracy for COVID-19 detection and increases the predictability rates for other chest diseases. The experimental results show that the proposed framework, when compared to other state-of-the-art models for diagnosing COVID-19 and other chest diseases, is more robust, and the results are promising. Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)
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19 pages, 5767 KiB  
Article
COVID-19 Lesion Segmentation Using Lung CT Scan Images: Comparative Study Based on Active Contour Models
by Younes Akbari, Hanadi Hassen, Somaya Al-Maadeed and Susu M. Zughaier
Appl. Sci. 2021, 11(17), 8039; https://doi.org/10.3390/app11178039 - 30 Aug 2021
Cited by 15 | Viewed by 3009
Abstract
Pneumonia is a lung infection that threatens all age groups. In this paper, we use CT scans to investigate the effectiveness of active contour models (ACMs) for segmentation of pneumonia caused by the Coronavirus disease (COVID-19) as one of the successful methods for [...] Read more.
Pneumonia is a lung infection that threatens all age groups. In this paper, we use CT scans to investigate the effectiveness of active contour models (ACMs) for segmentation of pneumonia caused by the Coronavirus disease (COVID-19) as one of the successful methods for image segmentation. A comparison has been made between the performances of the state-of-the-art methods performed based on a database of lung CT scan images. This review helps the reader to identify starting points for research in the field of active contour models on COVID-19, which is a high priority for researchers and practitioners. Finally, the experimental results indicate that active contour methods achieve promising results when there are not enough images to use deep learning-based methods as one of the powerful tools for image segmentation. Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)
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17 pages, 4198 KiB  
Article
Multi-Agent Robot System to Monitor and Enforce Physical Distancing Constraints in Large Areas to Combat COVID-19 and Future Pandemics
by Syed Hammad Hussain Shah, Ole-Martin Hagen Steinnes, Eirik Gribbestad Gustafsson and Ibrahim A. Hameed
Appl. Sci. 2021, 11(16), 7200; https://doi.org/10.3390/app11167200 - 04 Aug 2021
Cited by 10 | Viewed by 1994
Abstract
Random outbreaks of infectious diseases in the past have left a persistent impact on societies. Currently, COVID-19 is spreading worldwide and consequently risking human lives. In this regard, maintaining physical distance has turned into an essential precautionary measure to curb the spread of [...] Read more.
Random outbreaks of infectious diseases in the past have left a persistent impact on societies. Currently, COVID-19 is spreading worldwide and consequently risking human lives. In this regard, maintaining physical distance has turned into an essential precautionary measure to curb the spread of the virus. In this paper, we propose an autonomous monitoring system that is able to enforce physical distancing rules in large areas round the clock without human intervention. We present a novel system to automatically detect groups of individuals who do not comply with physical distancing constraints, i.e., maintaining a distance of 1 m, by tracking them within large areas to re-identify them in case of repetitive non-compliance and enforcing physical distancing. We used a distributed network of multiple CCTV cameras mounted to the walls of buildings for the detection, tracking and re-identification of non-compliant groups. Furthermore, we used multiple self-docking autonomous robots with collision-free navigation to enforce physical distancing constraints by sending alert messages to those persons who are not adhering to physical distancing constraints. We conducted 28 experiments that included 15 participants in different scenarios to evaluate and highlight the performance and significance of the present system. The presented system is capable of re-identifying repetitive violations of physical distancing constraints by a non-compliant group, with high accuracy in terms of detection, tracking and localization through a set of coordinated CCTV cameras. Autonomous robots in the present system are capable of attending to non-compliant groups in multiple regions of a large area and encouraging them to comply with the constraints. Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)
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10 pages, 552 KiB  
Article
Complex Network Modelling of Origin–Destination Commuting Flows for the COVID-19 Epidemic Spread Analysis in Italian Lombardy Region
by Angela Lombardi, Nicola Amoroso, Alfonso Monaco, Sabina Tangaro and Roberto Bellotti
Appl. Sci. 2021, 11(10), 4381; https://doi.org/10.3390/app11104381 - 12 May 2021
Cited by 7 | Viewed by 2337
Abstract
Currently the whole world is affected by the COVID-19 disease. Italy was the first country to be seriously affected in Europe, where the first COVID-19 outbreak was localized in the Lombardy region. The further spreading of the cases led to the lockdown of [...] Read more.
Currently the whole world is affected by the COVID-19 disease. Italy was the first country to be seriously affected in Europe, where the first COVID-19 outbreak was localized in the Lombardy region. The further spreading of the cases led to the lockdown of the most affected regions in northern Italy and then the entire country. In this work we investigated an epidemic spread scenario in the Lombardy region by using the origin–destination matrix with information about the commuting flows among 1450 urban areas within the region. We performed a large-scale simulation-based modeling of the epidemic spread over the networks related to three main motivations, i.e., work, study and occasional transfers to quantify the potential contribution of each category of travellers to the spread of the epidemic process. Our findings outline that the three networks are characterised by different weight dynamic growth rates and that the network “work” has a critical role in the diffusion phenomenon showing the greatest contribution to the epidemic spread. Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)
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18 pages, 1446 KiB  
Article
Short-Term Prediction of COVID-19 Cases Using Machine Learning Models
by Md. Shahriare Satu, Koushik Chandra Howlader, Mufti Mahmud, M. Shamim Kaiser, Sheikh Mohammad Shariful Islam, Julian M. W. Quinn, Salem A. Alyami and Mohammad Ali Moni
Appl. Sci. 2021, 11(9), 4266; https://doi.org/10.3390/app11094266 - 08 May 2021
Cited by 47 | Viewed by 6153
Abstract
The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy [...] Read more.
The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh. Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)
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21 pages, 6225 KiB  
Article
COVID-19: Worldwide Profiles during the First 250 Days
by Nuno António, Paulo Rita and Pedro Saraiva
Appl. Sci. 2021, 11(8), 3400; https://doi.org/10.3390/app11083400 - 10 Apr 2021
Cited by 6 | Viewed by 2280
Abstract
The present COVID-19 pandemic is happening in a strongly interconnected world. This interconnection explains why it became universal in such a short period of time and why it stimulated the creation of a large amount of relevant open data. In this paper, we [...] Read more.
The present COVID-19 pandemic is happening in a strongly interconnected world. This interconnection explains why it became universal in such a short period of time and why it stimulated the creation of a large amount of relevant open data. In this paper, we use data science tools to explore this open data from the moment the pandemic began and across the first 250 days of prevalence before vaccination started. The use of unsupervised machine learning techniques allowed us to identify three clusters of countries and territories with similar profiles of standardized COVID-19 time dynamics. Although countries and territories in the three clusters share some characteristics, their composition is not homogenous. All these clusters contain countries from different geographies and with different development levels. The use of descriptive statistics and data visualization techniques enabled the description and understanding of where and how COVID-19 was impacting. Some interesting extracted features are discussed and suggestions for future research in this area are also presented. Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)
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29 pages, 2493 KiB  
Article
COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting
by Marwa Ben Jabra, Anis Koubaa, Bilel Benjdira, Adel Ammar and Habib Hamam
Appl. Sci. 2021, 11(6), 2884; https://doi.org/10.3390/app11062884 - 23 Mar 2021
Cited by 31 | Viewed by 5056
Abstract
The COVID-19 disease has spread all over the world, representing an intriguing challenge for humanity as a whole. The efficient diagnosis of humans infected by COVID-19 still remains an increasing need worldwide. The chest X-ray imagery represents, among others, one attractive means to [...] Read more.
The COVID-19 disease has spread all over the world, representing an intriguing challenge for humanity as a whole. The efficient diagnosis of humans infected by COVID-19 still remains an increasing need worldwide. The chest X-ray imagery represents, among others, one attractive means to detect COVID-19 cases efficiently. Many studies have reported the efficiency of using deep learning classifiers in diagnosing COVID-19 from chest X-ray images. They conducted several comparisons among a subset of classifiers to identify the most accurate. In this paper, we investigate the potential of the combination of state-of-the-art classifiers in achieving the highest possible accuracy for the detection of COVID-19 from X-ray. For this purpose, we conducted a comprehensive comparison study among 16 state-of-the-art classifiers. To the best of our knowledge, this is the first study considering this number of classifiers. This paper’s innovation lies in the methodology that we followed to develop the inference system that allows us to detect COVID-19 with high accuracy. The methodology consists of three steps: (1) comprehensive comparative study between 16 state-of-the-art classifiers; (2) comparison between different ensemble classification techniques, including hard/soft majority, weighted voting, Support Vector Machine, and Random Forest; and (3) finding the combination of deep learning models and ensemble classification techniques that lead to the highest classification confidence on three classes. We found that using the Majority Voting approach is an adequate strategy to adopt in general cases for this task and may achieve an average accuracy up to 99.314%. Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)
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19 pages, 1378 KiB  
Article
Does Two-Class Training Extract Real Features? A COVID-19 Case Study
by Luis Muñoz-Saavedra, Javier Civit-Masot, Francisco Luna-Perejón, Manuel Domínguez-Morales and Antón Civit
Appl. Sci. 2021, 11(4), 1424; https://doi.org/10.3390/app11041424 - 04 Feb 2021
Cited by 5 | Viewed by 1974
Abstract
Diagnosis aid systems that use image analysis are currently very useful due to the large workload of health professionals involved in making diagnoses. In recent years, Convolutional Neural Networks (CNNs) have been used to help in these tasks. For this reason, multiple studies [...] Read more.
Diagnosis aid systems that use image analysis are currently very useful due to the large workload of health professionals involved in making diagnoses. In recent years, Convolutional Neural Networks (CNNs) have been used to help in these tasks. For this reason, multiple studies that analyze the detection precision for several diseases have been developed. However, many of these works distinguish between only two classes: healthy and with a specific disease. Based on this premise, in this work, we try to answer the questions: When training an image classification system with only two classes (healthy and sick), does this system extract the specific features of this disease, or does it only obtain the features that differentiate it from a healthy patient? Trying to answer these questions, we analyze the particular case of COVID-19 detection. Many works that classify this disease using X-ray images have been published; some of them use two classes (with and without COVID-19), while others include more classes (pneumonia, SARS, influenza, etc.). In this work, we carry out several classification studies with two classes, using test images that do not belong to those classes, in order to try to answer the previous questions. The first studies indicate problems in these two-class systems when using a third class as a test, being classified inconsistently. Deeper studies show that deep learning systems trained with two classes do not correctly extract the characteristics of pathologies, but rather differentiate the classes based on the physical characteristics of the images. After the discussion, we conclude that these two-class trained deep learning systems are not valid if there are other diseases that cause similar symptoms. Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)
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23 pages, 9863 KiB  
Article
Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis
by Jannis Born, Nina Wiedemann, Manuel Cossio, Charlotte Buhre, Gabriel Brändle, Konstantin Leidermann, Julie Goulet, Avinash Aujayeb, Michael Moor, Bastian Rieck and Karsten Borgwardt
Appl. Sci. 2021, 11(2), 672; https://doi.org/10.3390/app11020672 - 12 Jan 2021
Cited by 98 | Viewed by 10252
Abstract
Care during the COVID-19 pandemic hinges upon the existence of fast, safe, and highly sensitive diagnostic tools. Considering significant practical advantages of lung ultrasound (LUS) over other imaging techniques, but difficulties for doctors in pattern recognition, we aim to leverage machine learning toward [...] Read more.
Care during the COVID-19 pandemic hinges upon the existence of fast, safe, and highly sensitive diagnostic tools. Considering significant practical advantages of lung ultrasound (LUS) over other imaging techniques, but difficulties for doctors in pattern recognition, we aim to leverage machine learning toward guiding diagnosis from LUS. We release the largest publicly available LUS dataset for COVID-19 consisting of 202 videos from four classes (COVID-19, bacterial pneumonia, non-COVID-19 viral pneumonia and healthy controls). On this dataset, we perform an in-depth study of the value of deep learning methods for the differential diagnosis of lung pathologies. We propose a frame-based model that correctly distinguishes COVID-19 LUS videos from healthy and bacterial pneumonia data with a sensitivity of 0.90±0.08 and a specificity of 0.96±0.04. To investigate the utility of the proposed method, we employ interpretability methods for the spatio-temporal localization of pulmonary biomarkers, which are deemed useful for human-in-the-loop scenarios in a blinded study with medical experts. Aiming for robustness, we perform uncertainty estimation and demonstrate the model to recognize low-confidence situations which also improves performance. Lastly, we validated our model on an independent test dataset and report promising performance (sensitivity 0.806, specificity 0.962). The provided dataset facilitates the validation of related methodology in the community and the proposed framework might aid the development of a fast, accessible screening method for pulmonary diseases. Dataset and all code are publicly available at: https://github.com/BorgwardtLab/covid19_ultrasound. Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)
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21 pages, 4875 KiB  
Article
Hybrid Deep Learning-Based Epidemic Prediction Framework of COVID-19: South Korea Case
by Firda Rahmadani and Hyunsoo Lee
Appl. Sci. 2020, 10(23), 8539; https://doi.org/10.3390/app10238539 - 29 Nov 2020
Cited by 14 | Viewed by 3396
Abstract
The emergence of COVID-19 and the pandemic have changed and devastated every aspect of our lives. Before effective vaccines are widely used, it is important to predict the epidemic patterns of COVID-19. As SARS-CoV-2 is transferred primarily by droplets of infected people, the [...] Read more.
The emergence of COVID-19 and the pandemic have changed and devastated every aspect of our lives. Before effective vaccines are widely used, it is important to predict the epidemic patterns of COVID-19. As SARS-CoV-2 is transferred primarily by droplets of infected people, the incorporation of human mobility is crucial in epidemic dynamics models. This study expands the susceptible–exposed–infected–recovered compartment model by considering human mobility among a number of regions. Although the expanded meta-population epidemic model exhibits better performance than general compartment models, it requires a more accurate estimation of the extended modeling parameters. To estimate the parameters of these epidemic models, the meta-population model is incorporated with deep learning models. The combined deep learning model generates more accurate modeling parameters, which are used for epidemic meta-population modeling. In order to demonstrate the effectiveness of the proposed hybrid deep learning framework, COVID-19 data in South Korea were tested, and the forecast of the epidemic patterns was compared with other estimation methods. Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)
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9 pages, 548 KiB  
Article
Fractional-Order SIR Epidemic Model for Transmission Prediction of COVID-19 Disease
by Kamil Kozioł, Rafał Stanisławski and Grzegorz Bialic
Appl. Sci. 2020, 10(23), 8316; https://doi.org/10.3390/app10238316 - 24 Nov 2020
Cited by 30 | Viewed by 2714
Abstract
In this paper, the fractional-order generalization of the susceptible-infected-recovered (SIR) epidemic model for predicting the spread of the COVID-19 disease is presented. The time-domain model implementation is based on the fixed-step method using the nabla fractional-order difference defined by Grünwald-Letnikov formula. We study [...] Read more.
In this paper, the fractional-order generalization of the susceptible-infected-recovered (SIR) epidemic model for predicting the spread of the COVID-19 disease is presented. The time-domain model implementation is based on the fixed-step method using the nabla fractional-order difference defined by Grünwald-Letnikov formula. We study the influence of fractional order values on the dynamic properties of the proposed fractional-order SIR model. In modeling the COVID-19 transmission, the model’s parameters are estimated while using the genetic algorithm. The model prediction results for the spread of COVID-19 in Italy and Spain confirm the usefulness of the introduced methodology. Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)
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29 pages, 12776 KiB  
Article
DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
by Mahdi Rezaei and Mohsen Azarmi
Appl. Sci. 2020, 10(21), 7514; https://doi.org/10.3390/app10217514 - 26 Oct 2020
Cited by 134 | Viewed by 16703
Abstract
Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-m physical distancing as a mandatory safety measure in shopping centres, [...] Read more.
Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-m physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a hybrid Computer Vision and YOLOv4-based Deep Neural Network (DNN) model for automated people detection in the crowd in indoor and outdoor environments using common CCTV security cameras. The proposed DNN model in combination with an adapted inverse perspective mapping (IPM) technique and SORT tracking algorithm leads to a robust people detection and social distancing monitoring. The model has been trained against two most comprehensive datasets by the time of the research—the Microsoft Common Objects in Context (MS COCO) and Google Open Image datasets. The system has been evaluated against the Oxford Town Centre dataset (including 150,000 instances of people detection) with superior performance compared to three state-of-the-art methods. The evaluation has been conducted in challenging conditions, including occlusion, partial visibility, and under lighting variations with the mean average precision of 99.8% and the real-time speed of 24.1 fps. We also provide an online infection risk assessment scheme by statistical analysis of the spatio-temporal data from people’s moving trajectories and the rate of social distancing violations. We identify high-risk zones with the highest possibility of virus spread and infection. This may help authorities to redesign the layout of a public place or to take precaution actions to mitigate high-risk zones. The developed model is a generic and accurate people detection and tracking solution that can be applied in many other fields such as autonomous vehicles, human action recognition, anomaly detection, sports, crowd analysis, or any other research areas where the human detection is in the centre of attention. Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)
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19 pages, 1215 KiB  
Article
Evaluating the Effectiveness of COVID-19 Bluetooth-Based Smartphone Contact Tracing Applications
by Enrique Hernández-Orallo, Carlos T. Calafate, Juan-Carlos Cano and Pietro Manzoni
Appl. Sci. 2020, 10(20), 7113; https://doi.org/10.3390/app10207113 - 13 Oct 2020
Cited by 39 | Viewed by 7214
Abstract
One of the strategies to control the spread of infectious diseases is based on the use of specialized applications for smartphones. These apps offer the possibility, once individuals are detected to be infected, to trace their previous contacts in order to test and [...] Read more.
One of the strategies to control the spread of infectious diseases is based on the use of specialized applications for smartphones. These apps offer the possibility, once individuals are detected to be infected, to trace their previous contacts in order to test and detect new possibly-infected individuals. This paper evaluates the effectiveness of recently developed contact tracing smartphone applications for COVID-19 that rely on Bluetooth to detect contacts. We study how these applications work in order to model the main aspects that can affect their performance: precision, utilization, tracing speed and implementation model (centralized vs. decentralized). Then, we propose an epidemic model to evaluate their efficiency in terms of controlling future outbreaks and the effort required (e.g., individuals quarantined). Our results show that smartphone contact tracing can only be effective when combined with other mild measures that can slightly reduce the reproductive number R0 (for example, social distancing). Furthermore, we have found that a centralized model is much more effective, requiring an application utilization percentage of about 50% to control an outbreak. On the contrary, a decentralized model would require a higher utilization to be effective. Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)
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12 pages, 1672 KiB  
Article
COVID-XNet: A Custom Deep Learning System to Diagnose and Locate COVID-19 in Chest X-ray Images
by Lourdes Duran-Lopez, Juan Pedro Dominguez-Morales, Jesús Corral-Jaime, Saturnino Vicente-Diaz and Alejandro Linares-Barranco
Appl. Sci. 2020, 10(16), 5683; https://doi.org/10.3390/app10165683 - 16 Aug 2020
Cited by 65 | Viewed by 5705
Abstract
The COVID-19 pandemic caused by the new coronavirus SARS-CoV-2 has changed the world as we know it. An early diagnosis is crucial in order to prevent new outbreaks and control its rapid spread. Medical imaging techniques, such as X-ray or chest computed tomography, [...] Read more.
The COVID-19 pandemic caused by the new coronavirus SARS-CoV-2 has changed the world as we know it. An early diagnosis is crucial in order to prevent new outbreaks and control its rapid spread. Medical imaging techniques, such as X-ray or chest computed tomography, are commonly used for this purpose due to their reliability for COVID-19 diagnosis. Computer-aided diagnosis systems could play an essential role in aiding radiologists in the screening process. In this work, a novel Deep Learning-based system, called COVID-XNet, is presented for COVID-19 diagnosis in chest X-ray images. The proposed system performs a set of preprocessing algorithms to the input images for variability reduction and contrast enhancement, which are then fed to a custom Convolutional Neural Network in order to extract relevant features and perform the classification between COVID-19 and normal cases. The system is trained and validated using a 5-fold cross-validation scheme, achieving an average accuracy of 94.43% and an AUC of 0.988. The output of the system can be visualized using Class Activation Maps, highlighting the main findings for COVID-19 in X-ray images. These promising results indicate that COVID-XNet could be used as a tool to aid radiologists and contribute to the fight against COVID-19. Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)
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10 pages, 1897 KiB  
Article
Deep Learning System for COVID-19 Diagnosis Aid Using X-ray Pulmonary Images
by Javier Civit-Masot, Francisco Luna-Perejón, Manuel Domínguez Morales and Anton Civit
Appl. Sci. 2020, 10(13), 4640; https://doi.org/10.3390/app10134640 - 05 Jul 2020
Cited by 120 | Viewed by 10133
Abstract
The spread of the SARS-CoV-2 virus has made the COVID-19 disease a worldwide epidemic. The most common tests to identify COVID-19 are invasive, time consuming and limited in resources. Imaging is a non-invasive technique to identify if individuals have symptoms of disease in [...] Read more.
The spread of the SARS-CoV-2 virus has made the COVID-19 disease a worldwide epidemic. The most common tests to identify COVID-19 are invasive, time consuming and limited in resources. Imaging is a non-invasive technique to identify if individuals have symptoms of disease in their lungs. However, the diagnosis by this method needs to be made by a specialist doctor, which limits the mass diagnosis of the population. Image processing tools to support diagnosis reduce the load by ruling out negative cases. Advanced artificial intelligence techniques such as Deep Learning have shown high effectiveness in identifying patterns such as those that can be found in diseased tissue. This study analyzes the effectiveness of a VGG16-based Deep Learning model for the identification of pneumonia and COVID-19 using torso radiographs. Results show a high sensitivity in the identification of COVID-19, around 100%, and with a high degree of specificity, which indicates that it can be used as a screening test. AUCs on ROC curves are greater than 0.9 for all classes considered. Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)
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Jump to: Editorial, Research

1 pages, 158 KiB  
Erratum
Erratum: Born et al. Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis. Appl. Sci. 2021, 11, 672
by Jannis Born, Nina Wiedemann, Manuel Cossio, Charlotte Buhre, Gabriel Brändle, Konstantin Leidermann, Julie Goulet, Avinash Aujayeb, Michael Moor, Bastian Rieck and Karsten Borgwardt
Appl. Sci. 2022, 12(8), 3869; https://doi.org/10.3390/app12083869 - 12 Apr 2022
Viewed by 1174
Abstract
The authors wish to make the following corrections to this paper [...] Full article
(This article belongs to the Special Issue Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis)
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