Calcification detection for intravascular ultrasound image using direct acyclic graph architecture: pre-trained model for 1-channel image
Coronary artery calcification is a calcium buildup within the walls of the arteries. It is considered a predominant marker for coronary artery disease. Thus many approaches have been developed for the automatic detection of calcification. The previous calcification detection was on segmentation of o...
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Institute of Advanced Engineering and Science,
2021-05-01.
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| LEADER | 03440 am a22004213u 4500 | ||
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| 042 | |a dc | ||
| 100 | 1 | 0 | |a Sofian, Hannah |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Ming Than, Joel Chia |e author |
| 700 | 1 | 0 | |a Mohamad, Suraya |e author |
| 700 | 1 | 0 | |a Mohd Noor, Norliza |e author |
| 245 | 0 | 0 | |a Calcification detection for intravascular ultrasound image using direct acyclic graph architecture: pre-trained model for 1-channel image |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-05-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23340 | ||
| 520 | |a Coronary artery calcification is a calcium buildup within the walls of the arteries. It is considered a predominant marker for coronary artery disease. Thus many approaches have been developed for the automatic detection of calcification. The previous calcification detection was on segmentation of other structures as pre-processing steps or using the fact that the calcification often appears as a bright region. In this paper, an automated system proposed using a deep learning approach to detect the calcification absence and calcification presence in coronary artery IVUS image. A useful advantage of deep learning, compared to other methods is, it uses representations and features directly from the raw data, bypassing the need to manually extract features, a common that required in the traditional machine learning framework. The type of deep learning architecture used is 27 layers of convolutional neural networks (CNNs) using Direct Acyclic Graph. The proposed system used 2175 images and achieved an accuracy of 98.16% for Cartesian coordinate images and 99.08% for Polar Reconstructed Coordinate images. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Coronary Artery Disease; Calcification; Transformed Image; Transfer Learning; Direct Acyclic Graph | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 22, No 2: May 2021; 787-794 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v22.i2 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23340/14965 | |
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