Automated Clinical Decision Support for Coronary Plaques Characterization from Optical Coherence Tomography Imaging with Fused Neural Networks
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 automate...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Optics |
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| Online Access: | https://www.mdpi.com/2673-3269/3/1/2 |
| _version_ | 1797443841365639168 |
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| author | Haroon Zafar Junaid Zafar Faisal Sharif |
| author_facet | Haroon Zafar Junaid Zafar Faisal Sharif |
| author_sort | Haroon Zafar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T13:03:54Z |
| format | Article |
| id | doaj.art-8161345916a248bba13fe10783585c0d |
| institution | Directory Open Access Journal |
| issn | 2673-3269 |
| language | English |
| last_indexed | 2024-03-09T13:03:54Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Optics |
| spelling | doaj.art-8161345916a248bba13fe10783585c0d2023-11-30T21:51:21ZengMDPI AGOptics2673-32692022-01-013181810.3390/opt3010002Automated Clinical Decision Support for Coronary Plaques Characterization from Optical Coherence Tomography Imaging with Fused Neural NetworksHaroon Zafar0Junaid Zafar1Faisal Sharif2Lambe Institute for Translational Research, National University of Ireland Galway, H91 YR71 Galway, IrelandFaculty of Engineering, Government College University, Lahore 54000, PakistanLambe Institute for Translational Research, National University of Ireland Galway, H91 YR71 Galway, IrelandDeep 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.https://www.mdpi.com/2673-3269/3/1/2optical coherence tomographyclassificationarterial plaqueslumenobjective functionconvolutional neural networks |
| spellingShingle | Haroon Zafar Junaid Zafar Faisal Sharif Automated Clinical Decision Support for Coronary Plaques Characterization from Optical Coherence Tomography Imaging with Fused Neural Networks Optics optical coherence tomography classification arterial plaques lumen objective function convolutional neural networks |
| title | Automated Clinical Decision Support for Coronary Plaques Characterization from Optical Coherence Tomography Imaging with Fused Neural Networks |
| title_full | Automated Clinical Decision Support for Coronary Plaques Characterization from Optical Coherence Tomography Imaging with Fused Neural Networks |
| title_fullStr | Automated Clinical Decision Support for Coronary Plaques Characterization from Optical Coherence Tomography Imaging with Fused Neural Networks |
| title_full_unstemmed | Automated Clinical Decision Support for Coronary Plaques Characterization from Optical Coherence Tomography Imaging with Fused Neural Networks |
| title_short | Automated Clinical Decision Support for Coronary Plaques Characterization from Optical Coherence Tomography Imaging with Fused Neural Networks |
| title_sort | automated clinical decision support for coronary plaques characterization from optical coherence tomography imaging with fused neural networks |
| topic | optical coherence tomography classification arterial plaques lumen objective function convolutional neural networks |
| url | https://www.mdpi.com/2673-3269/3/1/2 |
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