Automatic Classification of Bright Retinal Lesions via Deep Network Features - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2018

Automatic Classification of Bright Retinal Lesions via Deep Network Features

Résumé

The diabetic retinopathy is timely diagonalized through color eye fundus images by experienced ophthalmologists, in order to recognize potential retinal features and identify early-blindness cases. In this paper, it is proposed to extract deep features from the last fully-connected layer of, four different, pre-trained convolutional neural networks. These features are then feeded into a non-linear classifier to discriminate three-class diabetic cases, i.e., normal, exudates, and drusen. Averaged across 1113 color retinal images collected from six publicly available annotated datasets, the deep features approach perform better than the classical bag-of-words approach. The proposed approaches have an average accuracy between 91.23% and 92.00% with more than 13% improvement over the traditional state of art methods.

Dates et versions

hal-01690356 , version 1 (23-01-2018)

Identifiants

Citer

Ibrahim Sadek, Mohamed Elawady, Abd El Rahman Shabayek. Automatic Classification of Bright Retinal Lesions via Deep Network Features. 2018. ⟨hal-01690356⟩
101 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More