Automated stenosis classification of carotid artery sonography using deep neural networks

Tony Lindsey, Zsolt Garami

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Carotid duplex sonography is an ultrasound-based diagnostic imaging technique used to reveal structural details of carotid arteries for hemodynamic changes and plaque morphology assessment. Deep convolutional neural networks were utilized to classify sonographic images into four categories based on learned features of internal carotid artery blood flow rate. Rather than build networks from scratch, model weights from three pretrained architectures were utilized and fine-tuned to accelerate task learning. A 50 layer residual network was evaluated as a feature generator for carotid stenosis identification. Moreover, a Gaussian process model utilized expected improvement acquisition to perform global hyperparameter optimization. An adversarial network mitigated class imbalance by generating 2500 severe category images for data augmentation. Finally, an ensemble meta-learner was constructed that calculated a weighted average of classifier probabilities. Distinct sonographic datasets were utilized for building two 4-ary classification models that achieved peak test metric performance accuracies of 98.37% and 97.26% respectively.

Original languageEnglish (US)
Title of host publicationProceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
EditorsM. Arif Wani, Taghi M. Khoshgoftaar, Dingding Wang, Huanjing Wang, Naeem Seliya
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1880-1884
Number of pages5
ISBN (Electronic)9781728145495
DOIs
StatePublished - Dec 2019
Event18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 - Boca Raton, United States
Duration: Dec 16 2019Dec 19 2019

Publication series

NameProceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019

Other

Other18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
CountryUnited States
CityBoca Raton
Period12/16/1912/19/19

Keywords

  • Carotid stenosis
  • Classification
  • Deep learning
  • Ensemble
  • Gaussian process
  • Generative adversarial network

ASJC Scopus subject areas

  • Strategy and Management
  • Artificial Intelligence
  • Computer Science Applications
  • Decision Sciences (miscellaneous)
  • Signal Processing
  • Media Technology

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