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Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS

In recent years in medical imaging technology, the advancement for medical diagnosis, the initial assessment of the ailment, and the abnormality have become challenging for radiologists. Magnetic resonance imaging is one such predominant technology used extensively for the initial evaluation of ailm...

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Autores principales: Naga Srinivasu, Parvathaneni, Balas, Valentina Emilia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8356652/
https://www.ncbi.nlm.nih.gov/pubmed/34435099
http://dx.doi.org/10.7717/peerj-cs.654
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author Naga Srinivasu, Parvathaneni
Balas, Valentina Emilia
author_facet Naga Srinivasu, Parvathaneni
Balas, Valentina Emilia
author_sort Naga Srinivasu, Parvathaneni
collection PubMed
description In recent years in medical imaging technology, the advancement for medical diagnosis, the initial assessment of the ailment, and the abnormality have become challenging for radiologists. Magnetic resonance imaging is one such predominant technology used extensively for the initial evaluation of ailments. The primary goal is to mechanizean approach that can accurately assess the damaged region of the human brain throughan automated segmentation process that requires minimal training and can learn by itself from the previous experimental outcomes. It is computationally more efficient than other supervised learning strategies such as CNN deep learning models. As a result, the process of investigation and statistical analysis of the abnormality would be made much more comfortable and convenient. The proposed approach’s performance seems to be much better compared to its counterparts, with an accuracy of 77% with minimal training of the model. Furthermore, the performance of the proposed training model is evaluated through various performance evaluation metrics like sensitivity, specificity, the Jaccard Similarity Index, and the Matthews correlation coefficient, where the proposed model is productive with minimal training.
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spelling pubmed-83566522021-08-24 Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS Naga Srinivasu, Parvathaneni Balas, Valentina Emilia PeerJ Comput Sci Artificial Intelligence In recent years in medical imaging technology, the advancement for medical diagnosis, the initial assessment of the ailment, and the abnormality have become challenging for radiologists. Magnetic resonance imaging is one such predominant technology used extensively for the initial evaluation of ailments. The primary goal is to mechanizean approach that can accurately assess the damaged region of the human brain throughan automated segmentation process that requires minimal training and can learn by itself from the previous experimental outcomes. It is computationally more efficient than other supervised learning strategies such as CNN deep learning models. As a result, the process of investigation and statistical analysis of the abnormality would be made much more comfortable and convenient. The proposed approach’s performance seems to be much better compared to its counterparts, with an accuracy of 77% with minimal training of the model. Furthermore, the performance of the proposed training model is evaluated through various performance evaluation metrics like sensitivity, specificity, the Jaccard Similarity Index, and the Matthews correlation coefficient, where the proposed model is productive with minimal training. PeerJ Inc. 2021-08-02 /pmc/articles/PMC8356652/ /pubmed/34435099 http://dx.doi.org/10.7717/peerj-cs.654 Text en ©2021 Naga Srinivasu and Balas 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, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Artificial Intelligence
Naga Srinivasu, Parvathaneni
Balas, Valentina Emilia
Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS
title Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS
title_full Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS
title_fullStr Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS
title_full_unstemmed Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS
title_short Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS
title_sort self-learning network-based segmentation for real-time brain m.r. images through haris
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8356652/
https://www.ncbi.nlm.nih.gov/pubmed/34435099
http://dx.doi.org/10.7717/peerj-cs.654
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