Multicenter Validation of Convolutional Neural Networks for Automated Detection of Cardiomegaly on Chest Radiographs
This work focused on validating five convolutional neural network models to detect automatically cardiomegaly, a health complication that causes heart enlargement, which may lead to cardiac arrest. To do that, we trained the models with a customized multilayer perceptron. Radiographs from two public datasets were used in experiments, one of them only for external validation. Images were pre-processed to contain just the chest cavity. The EfficientNet model yielded the highest area under the curve (AUC) of 0.91 on the test set. However, the Inception-based model obtained the best generalization performance with AUC of 0.88 on the independent multicentric dataset. Therefore, this work accurately validated radiographic models to identify patients with cardiomegaly.
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