Automatic Classification of Bright Retinal Lesions via Deep Network Features

2017/07/07 by Ibrahim Sadek, Sadek, Ibrahim, Mohamed Elawady +3
Computer Science · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging for Blood Diseases #FOS: Computer and information sciences #Retinal Imaging and Analysis #Retinal and Optic Conditions

paper · pdf · doi:10.48550/arxiv.1707.02022

openalex publication_date 2017/07/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Return of the Devil in the Details: Delving Deep into Convolutional Nets

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