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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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 |
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TK Electrical engineering. Electronics Nuclear engineering |
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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 |
| _version_ |
1643655882531143680 |
| score |
13.251813 |