A pilot study: quantify lung volume and emphysema extent directly from 2-D scout images.
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Summary
Two classical convolutional neural networks were trained, including VGG19 and InceptionV3, to compute lung volume and the percentage of emphysema from the scout images to demonstrate the feasibility of inferring volume metrics from planar images using CNNs.
- Type
- article
- Published
- 2021-06-02
- Cited by
- 6
- References
- 39
- Access
- Open access
- OpenAlex
- https://openalex.org/W3164985361
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:235320794
Keywords
Lung volumes, COPD, Lung, Nuclear medicine, Medicine
References
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Cited by
- Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning
- Explainable emphysema detection on chest radiographs with deep learning
- Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review
- A Low-Cost Digital Stethoscope For Normal and Abnormal Heart Sound Classification
- XRayWizard: Reconstructing 3-D lung surfaces from a single 2-D chest x-ray image via Vision Transformer
- Simulation-Driven Annotation-Free Deep Learning for Automated Detection and Segmentation of Airway Mucus Plugs on Non-Contrast CT Images
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