Evaluation of a novel deep learning-based classifier for perifissural nodules.

ABSTRACT:

Objectives

To evaluate the performance of a novel convolutional neural network (CNN) for the classification of typical perifissural nodules (PFN).

Methods

Chest CT data from two centers in the UK and The Netherlands (1668 unique nodules, 1260 individuals) were collected. Pulmonary nodules were classified into subtypes, including "typical PFNs" on-site, and were reviewed by a central clinician. The dataset was divided into a training/cross-validation set of 1557 nodules (1103 individuals) and a test set of 196 nodules (158 individuals). For the test set, three radiologically trained readers classified the nodules into three nodule categories: typical PFN, atypical PFN, and non-PFN. The consensus of the three readers was used as reference to evaluate the performance of the PFN

SUBMITTER: Han D 

PROVIDER: S-EPMC8128854 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature