CNNs for automatic glaucoma assessment using fundus images: an extensive validation

CNNs for automatic glaucoma assessment using fundus images: an extensive validation

Abstract Background Most current algorithms for automatic glaucoma assessment using fundus images rely on handcrafted features based on segmentation, which are affected by the performance of the chosen segmentation method and the extracted features. Among other characteristics, convolutional neural...

Full description

Bibliographic Details
Main Authors: Andres Diaz-Pinto, Sandra Morales, Valery Naranjo, Thomas Köhler, Jose M. Mossi, Amparo Navea
Format: Article
Language:English
Published: BMC 2019-03-01
Series:BioMedical Engineering OnLine
Subjects:
Glaucoma
ACRIMA database
Fundus images
CNN
Fine-tuning
Online Access:http://link.springer.com/article/10.1186/s12938-019-0649-y

Internet

http://link.springer.com/article/10.1186/s12938-019-0649-y

Similar Items