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Purpose
Comparing the efficacy of a deep‐learning model in classifying the etiology of pneumonia on pediatric chest X‐rays (CXRs) with that of human readers.
Methods
We built a clinical‐pediatric CXR set containing 4035 patients to exploit a deep‐learning model called Resnet‐50 for differentiating viral from bacterial pneumonia. The dataset was split into training (80%) and validation (20%). Model...
Purpose
To evaluate the efficacy of a deep‐learning model to segment the lung and thorax regions in pediatric chest X‐rays (CXRs). Validating the diagnosis of bacterial or viral pneumonia could be improved after lung segmentation.
Materials and methods
A clinical‐pediatric CXR set including 1351 patients was proposed to develop a deep‐learning model for the pulmonary‐thoracic segmentations. Model...
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