Medical image analysis using deep learning: a review

Over the recent past, deep learning is one of the core research directions which has gained a great deal of attention due to its outstanding performance in the area of medical image analysis. This paper aims to present a review of deep learning concepts related to medical imaging. We examine the use...

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Main Authors:	Nisa, Syed Qamrun, Ismail, Amelia Ritahani, MD Ali, M. A. B., Khan, Mohammad Shadab
Format:	Conference or Workshop Item
Language:	English
Published:	IEEE 2019
Subjects:	QA75 Electronic computers. Computer science R Medicine (General)
Online Access:	http://irep.iium.edu.my/77180/ http://irep.iium.edu.my/77180/ http://irep.iium.edu.my/77180/8/77180%20Medical%20Image%20Analysis.pdf

id	iium-77180
recordtype	eprints
spelling	iium-771802020-02-03T02:46:52Z http://irep.iium.edu.my/77180/ Medical image analysis using deep learning: a review Nisa, Syed Qamrun Ismail, Amelia Ritahani MD Ali, M. A. B. Khan, Mohammad Shadab QA75 Electronic computers. Computer science R Medicine (General) Over the recent past, deep learning is one of the core research directions which has gained a great deal of attention due to its outstanding performance in the area of medical image analysis. This paper aims to present a review of deep learning concepts related to medical imaging. We examine the use of deep learning for medical image analysis including segmentation, object detection and classification. Deep learning techniques including convolutional neural networks (CNNs), recurrent neural network (RNNs) and auto-encoder (AE) are also discussed in this paper. IEEE 2019 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/77180/8/77180%20Medical%20Image%20Analysis.pdf Nisa, Syed Qamrun and Ismail, Amelia Ritahani and MD Ali, M. A. B. and Khan, Mohammad Shadab (2019) Medical image analysis using deep learning: a review. In: 2019 IEEE International Conference on Innovative Research and Development (ICIRD), 28th-30th June 2019, Depok, Indonesia. http://icird.etssm.org/wp-content/uploads/2019/06/booklet.pdf
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science R Medicine (General)
spellingShingle	QA75 Electronic computers. Computer science R Medicine (General) Nisa, Syed Qamrun Ismail, Amelia Ritahani MD Ali, M. A. B. Khan, Mohammad Shadab Medical image analysis using deep learning: a review
description	Over the recent past, deep learning is one of the core research directions which has gained a great deal of attention due to its outstanding performance in the area of medical image analysis. This paper aims to present a review of deep learning concepts related to medical imaging. We examine the use of deep learning for medical image analysis including segmentation, object detection and classification. Deep learning techniques including convolutional neural networks (CNNs), recurrent neural network (RNNs) and auto-encoder (AE) are also discussed in this paper.
format	Conference or Workshop Item
author	Nisa, Syed Qamrun Ismail, Amelia Ritahani MD Ali, M. A. B. Khan, Mohammad Shadab
author_facet	Nisa, Syed Qamrun Ismail, Amelia Ritahani MD Ali, M. A. B. Khan, Mohammad Shadab
author_sort	Nisa, Syed Qamrun
title	Medical image analysis using deep learning: a review
title_short	Medical image analysis using deep learning: a review
title_full	Medical image analysis using deep learning: a review
title_fullStr	Medical image analysis using deep learning: a review
title_full_unstemmed	Medical image analysis using deep learning: a review
title_sort	medical image analysis using deep learning: a review
publisher	IEEE
publishDate	2019
url	http://irep.iium.edu.my/77180/ http://irep.iium.edu.my/77180/ http://irep.iium.edu.my/77180/8/77180%20Medical%20Image%20Analysis.pdf
first_indexed	2023-09-18T21:48:54Z
last_indexed	2023-09-18T21:48:54Z
_version_	1777413635953393664

Medical image analysis using deep learning: a review

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