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A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA

We developed a framework to detect and grade knee RA using digital X-radiation images and used it to demonstrate the ability of deep learning approaches to detect knee RA using a consensus-based decision (CBD) grading system. The study aimed to evaluate the efficiency with which a deep learning appr...

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Autores principales: Srinivasan, Saravanan, Gunasekaran, Subathra, Mathivanan, Sandeep Kumar, Jayagopal, Prabhu, Khan, Muhammad Attique, Alasiry, Areej, Marzougui, Mehrez, Masood, Anum
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137623/
https://www.ncbi.nlm.nih.gov/pubmed/37189485
http://dx.doi.org/10.3390/diagnostics13081385
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author Srinivasan, Saravanan
Gunasekaran, Subathra
Mathivanan, Sandeep Kumar
Jayagopal, Prabhu
Khan, Muhammad Attique
Alasiry, Areej
Marzougui, Mehrez
Masood, Anum
author_facet Srinivasan, Saravanan
Gunasekaran, Subathra
Mathivanan, Sandeep Kumar
Jayagopal, Prabhu
Khan, Muhammad Attique
Alasiry, Areej
Marzougui, Mehrez
Masood, Anum
author_sort Srinivasan, Saravanan
collection PubMed
description We developed a framework to detect and grade knee RA using digital X-radiation images and used it to demonstrate the ability of deep learning approaches to detect knee RA using a consensus-based decision (CBD) grading system. The study aimed to evaluate the efficiency with which a deep learning approach based on artificial intelligence (AI) can find and determine the severity of knee RA in digital X-radiation images. The study comprised people over 50 years with RA symptoms, such as knee joint pain, stiffness, crepitus, and functional impairments. The digitized X-radiation images of the people were obtained from the BioGPS database repository. We used 3172 digital X-radiation images of the knee joint from an anterior–posterior perspective. The trained Faster-CRNN architecture was used to identify the knee joint space narrowing (JSN) area in digital X-radiation images and extract the features using ResNet-101 with domain adaptation. In addition, we employed another well-trained model (VGG16 with domain adaptation) for knee RA severity classification. Medical experts graded the X-radiation images of the knee joint using a consensus-based decision score. We trained the enhanced-region proposal network (ERPN) using this manually extracted knee area as the test dataset image. An X-radiation image was fed into the final model, and a consensus decision was used to grade the outcome. The presented model correctly identified the marginal knee JSN region with 98.97% of accuracy, with a total knee RA intensity classification accuracy of 99.10%, with a sensitivity of 97.3%, a specificity of 98.2%, a precision of 98.1%, and a dice score of 90.1% compared with other conventional models.
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spelling pubmed-101376232023-04-28 A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA Srinivasan, Saravanan Gunasekaran, Subathra Mathivanan, Sandeep Kumar Jayagopal, Prabhu Khan, Muhammad Attique Alasiry, Areej Marzougui, Mehrez Masood, Anum Diagnostics (Basel) Article We developed a framework to detect and grade knee RA using digital X-radiation images and used it to demonstrate the ability of deep learning approaches to detect knee RA using a consensus-based decision (CBD) grading system. The study aimed to evaluate the efficiency with which a deep learning approach based on artificial intelligence (AI) can find and determine the severity of knee RA in digital X-radiation images. The study comprised people over 50 years with RA symptoms, such as knee joint pain, stiffness, crepitus, and functional impairments. The digitized X-radiation images of the people were obtained from the BioGPS database repository. We used 3172 digital X-radiation images of the knee joint from an anterior–posterior perspective. The trained Faster-CRNN architecture was used to identify the knee joint space narrowing (JSN) area in digital X-radiation images and extract the features using ResNet-101 with domain adaptation. In addition, we employed another well-trained model (VGG16 with domain adaptation) for knee RA severity classification. Medical experts graded the X-radiation images of the knee joint using a consensus-based decision score. We trained the enhanced-region proposal network (ERPN) using this manually extracted knee area as the test dataset image. An X-radiation image was fed into the final model, and a consensus decision was used to grade the outcome. The presented model correctly identified the marginal knee JSN region with 98.97% of accuracy, with a total knee RA intensity classification accuracy of 99.10%, with a sensitivity of 97.3%, a specificity of 98.2%, a precision of 98.1%, and a dice score of 90.1% compared with other conventional models. MDPI 2023-04-10 /pmc/articles/PMC10137623/ /pubmed/37189485 http://dx.doi.org/10.3390/diagnostics13081385 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Srinivasan, Saravanan
Gunasekaran, Subathra
Mathivanan, Sandeep Kumar
Jayagopal, Prabhu
Khan, Muhammad Attique
Alasiry, Areej
Marzougui, Mehrez
Masood, Anum
A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA
title A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA
title_full A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA
title_fullStr A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA
title_full_unstemmed A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA
title_short A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA
title_sort framework of faster crnn and vgg16-enhanced region proposal network for detection and grade classification of knee ra
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137623/
https://www.ncbi.nlm.nih.gov/pubmed/37189485
http://dx.doi.org/10.3390/diagnostics13081385
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