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Automatic detection, segmentation and classification of Abdominal Aortic Aneurysm using deep learning

In this paper, an automated method for the detection, segmentation and classification of Abdominal Aortic Aneurysm (AAA) region in computed tomography (CT) images is introduced. Deep Belief Network (DBN) is applied for the purpose of AAA detection and severity classification in this study. Optimum p...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Ho, Aik Hong, Sheikh, Usman Ullah
التنسيق: Conference or Workshop Item
منشور في: IEEE 2016
الموضوعات:
TK Electrical engineering. Electronics Nuclear engineering
الوصول للمادة أونلاين:http://eprints.utm.my/id/eprint/67022/
http://dx.doi.org/10.1109/CSPA.2016.7515839
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id my.utm.67022
record_format eprints
spelling my.utm.670222017-07-26T08:07:32Z http://eprints.utm.my/id/eprint/67022/ Automatic detection, segmentation and classification of Abdominal Aortic Aneurysm using deep learning Ho, Aik Hong Sheikh, Usman Ullah TK Electrical engineering. Electronics Nuclear engineering In this paper, an automated method for the detection, segmentation and classification of Abdominal Aortic Aneurysm (AAA) region in computed tomography (CT) images is introduced. Deep Belief Network (DBN) is applied for the purpose of AAA detection and severity classification in this study. Optimum parameters for training the DBN are determined for the training data from the selected dataset. AAA region can be successfully segmented from the CT images and the result is comparable to the existing methods. IEEE 2016-01-03 Conference or Workshop Item PeerReviewed Ho, Aik Hong and Sheikh, Usman Ullah (2016) Automatic detection, segmentation and classification of Abdominal Aortic Aneurysm using deep learning. In: 2016 IEEE 12th International Colloquium on Signal Processing and its Applications (CSPA 2016), 04-06 Mar, 2016, Melaka, Malaysia. http://dx.doi.org/10.1109/CSPA.2016.7515839
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Ho, Aik Hong
Sheikh, Usman Ullah
Automatic detection, segmentation and classification of Abdominal Aortic Aneurysm using deep learning
description In this paper, an automated method for the detection, segmentation and classification of Abdominal Aortic Aneurysm (AAA) region in computed tomography (CT) images is introduced. Deep Belief Network (DBN) is applied for the purpose of AAA detection and severity classification in this study. Optimum parameters for training the DBN are determined for the training data from the selected dataset. AAA region can be successfully segmented from the CT images and the result is comparable to the existing methods.
format Conference or Workshop Item
author Ho, Aik Hong
Sheikh, Usman Ullah
author_facet Ho, Aik Hong
Sheikh, Usman Ullah
author_sort Ho, Aik Hong
title Automatic detection, segmentation and classification of Abdominal Aortic Aneurysm using deep learning
title_short Automatic detection, segmentation and classification of Abdominal Aortic Aneurysm using deep learning
title_full Automatic detection, segmentation and classification of Abdominal Aortic Aneurysm using deep learning
title_fullStr Automatic detection, segmentation and classification of Abdominal Aortic Aneurysm using deep learning
title_full_unstemmed Automatic detection, segmentation and classification of Abdominal Aortic Aneurysm using deep learning
title_sort automatic detection, segmentation and classification of abdominal aortic aneurysm using deep learning
publisher IEEE
publishDate 2016
url http://eprints.utm.my/id/eprint/67022/
http://dx.doi.org/10.1109/CSPA.2016.7515839
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score 13.250246

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