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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| 主要な著者: | , |
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| フォーマット: | Conference or Workshop Item |
| 出版事項: |
IEEE
2016
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| 主題: | |
| オンライン･アクセス: | http://eprints.utm.my/id/eprint/67022/ http://dx.doi.org/10.1109/CSPA.2016.7515839 |
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| 要約: | 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. |
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