Universiti Teknologi Malaysia Institutional Repository

Using deep learning for calculation detection in coronary artery disease intravascular ultrasound image

Sofian, H. and Than, J. C. M. and Mohammad, S. and Noor, N. M. (2019) Using deep learning for calculation detection in coronary artery disease intravascular ultrasound image. In: 5th International Conference on Green Design and Manufacture 2019, IConGDM 2019, 29-30 Apr 2019, Aston Tropicana Hotel Kota Bandung, Jawa Barat, Indonesia.

Full text not available from this repository.

Official URL: http://www.dx.doi.org/10.1063/1.5118129


Coronary artery calcification is also part of atherosclerosis in cardiovascular systems. In this study, we present an automatics deep learning systems to detect the presence and the absent of calcification in intravascular ultrasound (IVUS) images. The standard practice for radiologists and clinicians to detect calcification are using visual inspection. The proposed system used Convolutional Neural Networks (CNNs), named AlexNet model, with six types of classifiers (Support Vector Machine, Discriminant Analysis, Ensembles, Decision Tree, K-Nearest neighbour and Naïve Bayes). The dataset B from MICCAI challenge 2011 consists of 1643 with calcification absent and 530 images with calcification present is used to demonstrate the effectiveness of our proposed automatic deep learning approach. The performance measures recorded are Accuracy, Sensitivity, Specificity, Positive predictive value and Negative predictive value. The performance is compared to the ground truth provided by the MICCAI challenge 2011. For testing and training, the cross-validation k-fold used are 2, 3, 5 and 10. The accuracy of one is obtained using four classifiers namely Support Vector Machine, Discriminant Analysis, Ensembles and k-Nearest neighbour using a Cartesian coordinate image when taken with k-fold=10. This is followed by Decision Tree classifier with an accuracy of 0.9890 using Polar Reconstructed Warp Coordinate images and Naïve Bayes classifier with an accuracy of 0.8464 when using Cartesian Warp coordinate images.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:cardiovascular systems, coronary artery, Cartesian coordinate image
Subjects:T Technology > T Technology (General)
Divisions:Razak School of Engineering and Advanced Technology
ID Code:89389
Deposited By: Narimah Nawil
Deposited On:09 Feb 2021 12:26
Last Modified:09 Feb 2021 12:26

Repository Staff Only: item control page