Staff View: Transfer learning techniques for medical image analysis: A review

Transfer learning techniques for medical image analysis: A review

Medical imaging is a useful tool for disease detection and diagnostic imaging technology has enabled early diagnosis of medical conditions. Manual image analysis methods are labor-intense and they are susceptible to intra as well as inter-observer variability. Auto-mated medical image analysis techn...

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Main Authors:	Kora, Padmavathi, Ooi, Chui Ping, Faust, Oliver, Raghavendra, U., Gudigar, Anjan, Chan, Wai Yee, Meenakshi, K., Swaraja, K., Plawiak, Pawel, Acharya, U. Rajendra
Format:	Article
Published:	Elsevier 2022
Subjects:	R Medicine TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.um.edu.my/43094/
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spelling	my.um.eprints.430942023-08-28T08:28:54Z http://eprints.um.edu.my/43094/ Transfer learning techniques for medical image analysis: A review Kora, Padmavathi Ooi, Chui Ping Faust, Oliver Raghavendra, U. Gudigar, Anjan Chan, Wai Yee Meenakshi, K. Swaraja, K. Plawiak, Pawel Acharya, U. Rajendra R Medicine TA Engineering (General). Civil engineering (General) Medical imaging is a useful tool for disease detection and diagnostic imaging technology has enabled early diagnosis of medical conditions. Manual image analysis methods are labor-intense and they are susceptible to intra as well as inter-observer variability. Auto-mated medical image analysis techniques can overcome these limitations. In this review, we investigated Transfer Learning (TL) architectures for automated medical image analysis. We discovered that TL has been applied to a wide range of medical imaging tasks, such as segmentation, object identification, disease categorization, severity grading, to name a few. We could establish that TL provides high quality decision support and requires less training data when compared to traditional deep learning methods. These advantageous properties arise from the fact that TL models have already been trained on large generic datasets and a task specific dataset is only used to customize the model. This eliminates the need to train the models from scratch. Our review shows that AlexNet, ResNet, VGGNet, and Goo-gleNet are the most widely used TL models for medical image analysis. We found that these Elsevier 2022-01 Article PeerReviewed Kora, Padmavathi and Ooi, Chui Ping and Faust, Oliver and Raghavendra, U. and Gudigar, Anjan and Chan, Wai Yee and Meenakshi, K. and Swaraja, K. and Plawiak, Pawel and Acharya, U. Rajendra (2022) Transfer learning techniques for medical image analysis: A review. Biocybernetics and Biomedical Engineering, 42 (1). pp. 79-107. ISSN 0208-5216, DOI https://doi.org/10.1016/j.bbe.2021.11.004 <https://doi.org/10.1016/j.bbe.2021.11.004>. 10.1016/j.bbe.2021.11.004
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	R Medicine TA Engineering (General). Civil engineering (General)
spellingShingle	R Medicine TA Engineering (General). Civil engineering (General) Kora, Padmavathi Ooi, Chui Ping Faust, Oliver Raghavendra, U. Gudigar, Anjan Chan, Wai Yee Meenakshi, K. Swaraja, K. Plawiak, Pawel Acharya, U. Rajendra Transfer learning techniques for medical image analysis: A review
description	Medical imaging is a useful tool for disease detection and diagnostic imaging technology has enabled early diagnosis of medical conditions. Manual image analysis methods are labor-intense and they are susceptible to intra as well as inter-observer variability. Auto-mated medical image analysis techniques can overcome these limitations. In this review, we investigated Transfer Learning (TL) architectures for automated medical image analysis. We discovered that TL has been applied to a wide range of medical imaging tasks, such as segmentation, object identification, disease categorization, severity grading, to name a few. We could establish that TL provides high quality decision support and requires less training data when compared to traditional deep learning methods. These advantageous properties arise from the fact that TL models have already been trained on large generic datasets and a task specific dataset is only used to customize the model. This eliminates the need to train the models from scratch. Our review shows that AlexNet, ResNet, VGGNet, and Goo-gleNet are the most widely used TL models for medical image analysis. We found that these
format	Article
author	Kora, Padmavathi Ooi, Chui Ping Faust, Oliver Raghavendra, U. Gudigar, Anjan Chan, Wai Yee Meenakshi, K. Swaraja, K. Plawiak, Pawel Acharya, U. Rajendra
author_facet	Kora, Padmavathi Ooi, Chui Ping Faust, Oliver Raghavendra, U. Gudigar, Anjan Chan, Wai Yee Meenakshi, K. Swaraja, K. Plawiak, Pawel Acharya, U. Rajendra
author_sort	Kora, Padmavathi
title	Transfer learning techniques for medical image analysis: A review
title_short	Transfer learning techniques for medical image analysis: A review
title_full	Transfer learning techniques for medical image analysis: A review
title_fullStr	Transfer learning techniques for medical image analysis: A review
title_full_unstemmed	Transfer learning techniques for medical image analysis: A review
title_sort	transfer learning techniques for medical image analysis: a review
publisher	Elsevier
publishDate	2022
url	http://eprints.um.edu.my/43094/
_version_	1776247441547329536
score	13.211869

Transfer learning techniques for medical image analysis: A review

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