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Deep Learning Approaches for Automatic Localization in Medical Images
Recent revolutionary advances in deep learning (DL) have fueled several breakthrough achievements in various complicated computer vision tasks. The remarkable successes and achievements started in 2012 when deep learning neural networks (DNNs) outperformed the shallow machine learning models on a nu...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9259335/ https://www.ncbi.nlm.nih.gov/pubmed/35814554 http://dx.doi.org/10.1155/2022/6347307 |
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author | Alaskar, H. Hussain, A. Almaslukh, B. Vaiyapuri, T. Sbai, Z. Dubey, Arun Kumar |
author_facet | Alaskar, H. Hussain, A. Almaslukh, B. Vaiyapuri, T. Sbai, Z. Dubey, Arun Kumar |
author_sort | Alaskar, H. |
collection | PubMed |
description | Recent revolutionary advances in deep learning (DL) have fueled several breakthrough achievements in various complicated computer vision tasks. The remarkable successes and achievements started in 2012 when deep learning neural networks (DNNs) outperformed the shallow machine learning models on a number of significant benchmarks. Significant advances were made in computer vision by conducting very complex image interpretation tasks with outstanding accuracy. These achievements have shown great promise in a wide variety of fields, especially in medical image analysis by creating opportunities to diagnose and treat diseases earlier. In recent years, the application of the DNN for object localization has gained the attention of researchers due to its success over conventional methods, especially in object localization. As this has become a very broad and rapidly growing field, this study presents a short review of DNN implementation for medical images and validates its efficacy on benchmarks. This study presents the first review that focuses on object localization using the DNN in medical images. The key aim of this study was to summarize the recent studies based on the DNN for medical image localization and to highlight the research gaps that can provide worthwhile ideas to shape future research related to object localization tasks. It starts with an overview on the importance of medical image analysis and existing technology in this space. The discussion then proceeds to the dominant DNN utilized in the current literature. Finally, we conclude by discussing the challenges associated with the application of the DNN for medical image localization which can drive further studies in identifying potential future developments in the relevant field of study. |
format | Online Article Text |
id | pubmed-9259335 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92593352022-07-07 Deep Learning Approaches for Automatic Localization in Medical Images Alaskar, H. Hussain, A. Almaslukh, B. Vaiyapuri, T. Sbai, Z. Dubey, Arun Kumar Comput Intell Neurosci Review Article Recent revolutionary advances in deep learning (DL) have fueled several breakthrough achievements in various complicated computer vision tasks. The remarkable successes and achievements started in 2012 when deep learning neural networks (DNNs) outperformed the shallow machine learning models on a number of significant benchmarks. Significant advances were made in computer vision by conducting very complex image interpretation tasks with outstanding accuracy. These achievements have shown great promise in a wide variety of fields, especially in medical image analysis by creating opportunities to diagnose and treat diseases earlier. In recent years, the application of the DNN for object localization has gained the attention of researchers due to its success over conventional methods, especially in object localization. As this has become a very broad and rapidly growing field, this study presents a short review of DNN implementation for medical images and validates its efficacy on benchmarks. This study presents the first review that focuses on object localization using the DNN in medical images. The key aim of this study was to summarize the recent studies based on the DNN for medical image localization and to highlight the research gaps that can provide worthwhile ideas to shape future research related to object localization tasks. It starts with an overview on the importance of medical image analysis and existing technology in this space. The discussion then proceeds to the dominant DNN utilized in the current literature. Finally, we conclude by discussing the challenges associated with the application of the DNN for medical image localization which can drive further studies in identifying potential future developments in the relevant field of study. Hindawi 2022-06-29 /pmc/articles/PMC9259335/ /pubmed/35814554 http://dx.doi.org/10.1155/2022/6347307 Text en Copyright © 2022 H. Alaskar et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Alaskar, H. Hussain, A. Almaslukh, B. Vaiyapuri, T. Sbai, Z. Dubey, Arun Kumar Deep Learning Approaches for Automatic Localization in Medical Images |
title | Deep Learning Approaches for Automatic Localization in Medical Images |
title_full | Deep Learning Approaches for Automatic Localization in Medical Images |
title_fullStr | Deep Learning Approaches for Automatic Localization in Medical Images |
title_full_unstemmed | Deep Learning Approaches for Automatic Localization in Medical Images |
title_short | Deep Learning Approaches for Automatic Localization in Medical Images |
title_sort | deep learning approaches for automatic localization in medical images |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9259335/ https://www.ncbi.nlm.nih.gov/pubmed/35814554 http://dx.doi.org/10.1155/2022/6347307 |
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