Abstract
Deep Neural Networks (DNNs) are nurturing clinical decision support systems for the detection and accurate modeling of coronary arterial plaques. However, efficient plaque characterization in time-constrained settings is still an open problem. The purpose of this study is to develop a novel automated classification architecture viable for the real-time clinical detection and classification of coronary artery plaques, and secondly, to use the novel dataset of OCT images for data augmentation. Further, the purpose is to validate the efficacy of transfer learning for arterial plaques classification. In this perspective, a novel time-efficient classification architecture based on DNNs is proposed. A new data set consisting of in-vivo patient Optical Coherence Tomography (OCT) images labeled by three trained experts was created and dynamically programmed. Generative Adversarial Networks (GANs) were used for populating the coronary aerial plaques dataset. We removed the fully connected layers, including softmax and the cross-entropy in the GoogleNet framework, and replaced them with the Support Vector Machines (SVMs). Our proposed architecture limits weight up-gradation cycles to only modified layers and computes the global hyper-plane in a timely, competitive fashion. Transfer learning was used for high-level discriminative feature learning. Cross-entropy loss was minimized by using the Adam optimizer for model training. A train validation scheme was used to determine the classification accuracy. Automated plaques differentiation in addition to their detection was found to agree with the clinical findings. Our customized fused classification scheme outperforms the other leading reported works with an overall accuracy of 96.84%, and multiple folds reduced elapsed time demonstrating it as a viable choice for real-time clinical settings.
Highlights
According to the World Health Organization (WHO), 85% of these deaths were due to plaque buildup that resulted in the narrowing of the coronary arteries through a process termed atherosclerosis
Data Augmentation through Generative Adversarial Networks (GANs) has proven handy for our limited arterial plaques dataset
We presented an in-depth exploration of plaque detection in Optical Coherence Tomography (OCT) pullbacks using hybrid CNNs
Summary
Deep Neural Networks (DNNs) are fueling medical imaging technology, medical diagnostics, and healthcare in general [1]. DNNs continue to be of significance in the field of cardiovascular imaging for the detection of coronary arterial plaques [2,3]. Coronary plaques are cholesterol deposits in the wall of the heart arteries and are the leading cause of death globally (projected one in four deaths). According to the World Health Organization (WHO), 85% of these deaths were due to plaque buildup that resulted in the narrowing of the coronary arteries through a process termed atherosclerosis. According to the World Health Organization (WHO), 85% of these deaths were due to plaque buildup that resulted in the narrowing of the coronary arteries through a process termed atherosclerosis. 4.0/).
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