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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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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. |
format | Online Article Text |
id | pubmed-9778629 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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