Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs.

ABSTRACT: Purpose To assess the ability of convolutional neural networks (CNNs) to enable high-performance automated binary classification of chest radiographs. Materials and Methods In a retrospective study, 216 431 frontal chest radiographs obtained between 1998 and 2012 were procured, along with associated text reports and a prospective label from the attending radiologist. This data set was used to train CNNs to classify chest radiographs as normal or abnormal before evaluation on a held-out set of 533 images hand-labeled by expert radiologists. The effects of development set size, training set size, initialization strategy, and network architecture on end performance were assessed by using standard binary classification metrics; detailed error analysis, including visualization of CNN activation

SUBMITTER: Dunnmon JA 

PROVIDER: S-EPMC6358056 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature