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.
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Official URL: http://dx.doi.org/10.1109/CSPA.2016.7515839
Abstract
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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information: | RADIS System Ref No:PB/2016/09427 |
| Uncontrolled Keywords: | deep belief network, segmentation |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Advanced Informatics School |
| ID Code: | 67022 |
| Deposited By: | Siti Nor Hashidah Zakaria |
| Deposited On: | 20 Jul 2017 04:10 |
| Last Modified: | 26 Jul 2017 08:07 |
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