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Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust

Due to the severity and speed of spread of the ongoing Covid-19 pandemic, fast but accurate diagnosis of Covid-19 patients has become a crucial task. Achievements in this respect might enlighten future efforts for the containment of other possible pandemics. Researchers from various fields have been...

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Autores principales: Sailunaz, Kashfia, Bestepe, Deniz, Özyer, Tansel, Rokne, Jon, Alhajj, Reda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778629/
https://www.ncbi.nlm.nih.gov/pubmed/36548288
http://dx.doi.org/10.1371/journal.pone.0278487
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author Sailunaz, Kashfia
Bestepe, Deniz
Özyer, Tansel
Rokne, Jon
Alhajj, Reda
author_facet Sailunaz, Kashfia
Bestepe, Deniz
Özyer, Tansel
Rokne, Jon
Alhajj, Reda
author_sort Sailunaz, Kashfia
collection PubMed
description Due to the severity and speed of spread of the ongoing Covid-19 pandemic, fast but accurate diagnosis of Covid-19 patients has become a crucial task. Achievements in this respect might enlighten future efforts for the containment of other possible pandemics. Researchers from various fields have been trying to provide novel ideas for models or systems to identify Covid-19 patients from different medical and non-medical data. AI-based researchers have also been trying to contribute to this area by mostly providing novel approaches of automated systems using convolutional neural network (CNN) and deep neural network (DNN) for Covid-19 detection and diagnosis. Due to the efficiency of deep learning (DL) and transfer learning (TL) models in classification and segmentation tasks, most of the recent AI-based researches proposed various DL and TL models for Covid-19 detection and infected region segmentation from chest medical images like X-rays or CT images. This paper describes a web-based application framework for Covid-19 lung infection detection and segmentation. The proposed framework is characterized by a feedback mechanism for self learning and tuning. It uses variations of three popular DL models, namely Mask R-CNN, U-Net, and U-Net++. The models were trained, evaluated and tested using CT images of Covid patients which were collected from two different sources. The web application provide a simple user friendly interface to process the CT images from various resources using the chosen models, thresholds and other parameters to generate the decisions on detection and segmentation. The models achieve high performance scores for Dice similarity, Jaccard similarity, accuracy, loss, and precision values. The U-Net model outperformed the other models with more than 98% accuracy.
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spelling pubmed-97786292022-12-23 Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust Sailunaz, Kashfia Bestepe, Deniz Özyer, Tansel Rokne, Jon Alhajj, Reda PLoS One Research Article Due to the severity and speed of spread of the ongoing Covid-19 pandemic, fast but accurate diagnosis of Covid-19 patients has become a crucial task. Achievements in this respect might enlighten future efforts for the containment of other possible pandemics. Researchers from various fields have been trying to provide novel ideas for models or systems to identify Covid-19 patients from different medical and non-medical data. AI-based researchers have also been trying to contribute to this area by mostly providing novel approaches of automated systems using convolutional neural network (CNN) and deep neural network (DNN) for Covid-19 detection and diagnosis. Due to the efficiency of deep learning (DL) and transfer learning (TL) models in classification and segmentation tasks, most of the recent AI-based researches proposed various DL and TL models for Covid-19 detection and infected region segmentation from chest medical images like X-rays or CT images. This paper describes a web-based application framework for Covid-19 lung infection detection and segmentation. The proposed framework is characterized by a feedback mechanism for self learning and tuning. It uses variations of three popular DL models, namely Mask R-CNN, U-Net, and U-Net++. The models were trained, evaluated and tested using CT images of Covid patients which were collected from two different sources. The web application provide a simple user friendly interface to process the CT images from various resources using the chosen models, thresholds and other parameters to generate the decisions on detection and segmentation. The models achieve high performance scores for Dice similarity, Jaccard similarity, accuracy, loss, and precision values. The U-Net model outperformed the other models with more than 98% accuracy. Public Library of Science 2022-12-22 /pmc/articles/PMC9778629/ /pubmed/36548288 http://dx.doi.org/10.1371/journal.pone.0278487 Text en © 2022 Sailunaz et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Sailunaz, Kashfia
Bestepe, Deniz
Özyer, Tansel
Rokne, Jon
Alhajj, Reda
Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust
title Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust
title_full Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust
title_fullStr Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust
title_full_unstemmed Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust
title_short Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust
title_sort interactive framework for covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778629/
https://www.ncbi.nlm.nih.gov/pubmed/36548288
http://dx.doi.org/10.1371/journal.pone.0278487
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