Deep Transfer Learning for the Multilabel Classification of Chest X-ray Images.

ABSTRACT: Chest X-ray (CXR) is widely used to diagnose conditions affecting the chest, its contents, and its nearby structures. In this study, we used a private data set containing 1630 CXR images with disease labels; most of the images were disease-free, but the others contained multiple sites of abnormalities. Here, we used deep convolutional neural network (CNN) models to extract feature representations and to identify possible diseases in these images. We also used transfer learning combined with large open-source image data sets to resolve the problems of insufficient training data and optimize the classification model. The effects of different approaches of reusing pretrained weights (model finetuning and layer transfer), source data sets of different sizes and similarity levels to the target

SUBMITTER: Huang GH 

PROVIDER: S-EPMC9222116 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature