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Automated Pneumothorax Diagnosis Using Deep Neural Networks

  • Tony LindseyEmail author
  • Rebecca Lee
  • Ronald Grisell
  • Saul Vega
  • Sena Veazey
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

Thoracic ultrasound can provide information leading to rapid diagnosis of pneumothorax with improved accuracy over the standard physical examination and with higher sensitivity than anteroposterior chest radiography. However, the clinical interpretation of a patient medical image is highly operator dependent. Furthermore, remote environments, such as the battlefield or deep-space exploration, may lack expertise for diagnosing certain pathologies. We have developed an automated image interpretation pipeline for the analysis of thoracic ultrasound data and the classification of pneumothorax events to provide decision support in such situations. Our pipeline consists of image preprocessing, data augmentation, and deep learning architectures for medical diagnosis. In this work, we demonstrate that robust, accurate interpretation of chest images and video can be achieved using deep neural networks. A number of novel image processing techniques were employed to achieve this result. Affine transformations were applied for data augmentation. Hyperparameters were optimized for learning rate, dropout regularization, batch size, and epoch iteration by a sequential model-based Bayesian approach. In addition, we utilized pretrained architectures, applying transfer learning and fine-tuning techniques to fully connected layers. Our pipeline yielded binary classification validation accuracies of \(98.3\%\) for M-mode images and \(99.8\%\) with B-mode video frames.

Keywords

Deep learning Pneumothorax classification Ultrasound Transfer learning Bayesian optimization 

Notes

Acknowledgements

We acknowledge Jose Salinas, PhD, chief of the Clinical Decision Support and Automation research at the US Army Institute of Surgical Research, which provided laboratory and biomedical device equipment support. We also acknowledge Maria Serio-Melvin, MSN, and Army Col. Shawn Nessen, DO, FACS, for their invaluable trauma patient clinical insights.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tony Lindsey
    • 1
    • 2
    Email author
  • Rebecca Lee
    • 2
  • Ronald Grisell
    • 3
  • Saul Vega
    • 3
  • Sena Veazey
    • 3
  1. 1.Department of Biomedical InformaticsStanford UniversityPalo AltoUSA
  2. 2.NASA Ames Research CenterMountain ViewUSA
  3. 3.US Army Institute of Surgical ResearchSan AntonioUSA

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