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...

Szczegółowa specyfikacja

Opis bibliograficzny
Główni autorzy: Andres Diaz-Pinto, Sandra Morales, Valery Naranjo, Thomas Köhler, Jose M. Mossi, Amparo Navea
Format: Artykuł
Język:English
Wydane: BMC 2019-03-01
Seria:BioMedical Engineering OnLine
Hasła przedmiotowe:
Glaucoma
ACRIMA database
Fundus images
CNN
Fine-tuning
Dostęp online:http://link.springer.com/article/10.1186/s12938-019-0649-y

Internet

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

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