A retrospective study of deep learning generalization across two centers and multiple models of X-ray devices using COVID-19 chest-X rays.

ABSTRACT: Generalization of deep learning (DL) algorithms is critical for the secure implementation of computer-aided diagnosis systems in clinical practice. However, broad generalization remains to be a challenge in machine learning. This research aims to identify and study potential factors that can affect the internal validation and generalization of DL networks, namely the institution where the images come from, the image processing applied by the X-ray device, and the type of response function of the X-ray device. For these purposes, a pre-trained convolutional neural network (CNN) (VGG16) was trained three times for classifying COVID-19 and control chest radiographs with the same hyperparameters, but using different combinations of data acquired in two institutions by three different X-ray dev

SUBMITTER: Fernandez-Miranda PM 

PROVIDER: S-EPMC11199585 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature