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A review on deep learning in medical image analysis

Ongoing improvements in AI, particularly concerning deep learning techniques, are assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the quickest developing field in artificial intelligence and is effectively utilized lately in numerous areas, including medic...

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Detalles Bibliográficos
Autores principales: Suganyadevi, S., Seethalakshmi, V., Balasamy, K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer London 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8417661/
https://www.ncbi.nlm.nih.gov/pubmed/34513553
http://dx.doi.org/10.1007/s13735-021-00218-1
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author Suganyadevi, S.
Seethalakshmi, V.
Balasamy, K.
author_facet Suganyadevi, S.
Seethalakshmi, V.
Balasamy, K.
author_sort Suganyadevi, S.
collection PubMed
description Ongoing improvements in AI, particularly concerning deep learning techniques, are assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the quickest developing field in artificial intelligence and is effectively utilized lately in numerous areas, including medication. A brief outline is given on studies carried out on the region of application: neuro, brain, retinal, pneumonic, computerized pathology, bosom, heart, breast, bone, stomach, and musculoskeletal. For information exploration, knowledge deployment, and knowledge-based prediction, deep learning networks can be successfully applied to big data. In the field of medical image processing methods and analysis, fundamental information and state-of-the-art approaches with deep learning are presented in this paper. The primary goals of this paper are to present research on medical image processing as well as to define and implement the key guidelines that are identified and addressed.
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spelling pubmed-84176612021-09-07 A review on deep learning in medical image analysis Suganyadevi, S. Seethalakshmi, V. Balasamy, K. Int J Multimed Inf Retr Regular Paper Ongoing improvements in AI, particularly concerning deep learning techniques, are assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the quickest developing field in artificial intelligence and is effectively utilized lately in numerous areas, including medication. A brief outline is given on studies carried out on the region of application: neuro, brain, retinal, pneumonic, computerized pathology, bosom, heart, breast, bone, stomach, and musculoskeletal. For information exploration, knowledge deployment, and knowledge-based prediction, deep learning networks can be successfully applied to big data. In the field of medical image processing methods and analysis, fundamental information and state-of-the-art approaches with deep learning are presented in this paper. The primary goals of this paper are to present research on medical image processing as well as to define and implement the key guidelines that are identified and addressed. Springer London 2021-09-04 2022 /pmc/articles/PMC8417661/ /pubmed/34513553 http://dx.doi.org/10.1007/s13735-021-00218-1 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Regular Paper
Suganyadevi, S.
Seethalakshmi, V.
Balasamy, K.
A review on deep learning in medical image analysis
title A review on deep learning in medical image analysis
title_full A review on deep learning in medical image analysis
title_fullStr A review on deep learning in medical image analysis
title_full_unstemmed A review on deep learning in medical image analysis
title_short A review on deep learning in medical image analysis
title_sort review on deep learning in medical image analysis
topic Regular Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8417661/
https://www.ncbi.nlm.nih.gov/pubmed/34513553
http://dx.doi.org/10.1007/s13735-021-00218-1
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