Cascaded classifiers and stacking methods for classification of pulmonary nodule characteristics


KAYA A.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol.166, pp.77-89, 2018 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 166
  • Publication Date: 2018
  • Doi Number: 10.1016/j.cmpb.2018.10.009
  • Title of Journal : COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
  • Page Numbers: pp.77-89

Abstract

Background and Objectives: Detection and classification of pulmonary nodules are critical tasks in medical image analysis. The Lung Image Database Consortium (LIDC) database is a widely used resource for small pulmonary nodule classification research. This dataset is comprised of nodule characteristic evaluations and CT scans of patients. Although these characteristics are utilized in several studies, they can be used to improve classification performance.