Cargando…
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...
Autores principales: | , |
---|---|
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 |
_version_ | 1783736986896433152 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8356652 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT nagasrinivasuparvathaneni selflearningnetworkbasedsegmentationforrealtimebrainmrimagesthroughharis AT balasvalentinaemilia selflearningnetworkbasedsegmentationforrealtimebrainmrimagesthroughharis |