27 February 2018 Boosting CNN performance for lung texture classification using connected filtering
Author Affiliations +
Abstract
Infiltrative lung diseases describe a large group of irreversible lung disorders requiring regular follow-up with CT imaging. Quantifying the evolution of the patient status imposes the development of automated classification tools for lung texture. This paper presents an original image pre-processing framework based on locally connected filtering applied in multiresolution, which helps improving the learning process and boost the performance of CNN for lung texture classification. By removing the dense vascular network from images used by the CNN for lung classification, locally connected filters provide a better discrimination between different lung patterns and help regularizing the classification output. The approach was tested in a preliminary evaluation on a 10 patient database of various lung pathologies, showing an increase of 10% in true positive rate (on average for all the cases) with respect to the state of the art cascade of CNNs for this task.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sebastián Roberto Tarando, Catalin Fetita, Young-Wouk Kim, Hyoun Cho, Pierre-Yves Brillet, "Boosting CNN performance for lung texture classification using connected filtering", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057505 (27 February 2018); doi: 10.1117/12.2293093; https://doi.org/10.1117/12.2293093
PROCEEDINGS
13 PAGES


SHARE
Back to Top