Naslov
Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion

Autori
Prentašić, Pavle ; Lončarić, Sven

Izvornik
Computer methods and programs in biomedicine (0169-2607) 137 (2016); 281-292

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
diabetic retinopathy ; exudates ; machine learning ; convolutional neural networks ; fundus photographs

Sažetak
Background and objective Diabetic retinopathy is one of the leading disabling chronic diseases and one of the leading causes of preventable blindness in developed world. Early diagnosis of diabetic retinopathy enables timely treatment and in order to achieve it a major effort will have to be invested into automated population screening programs. Detection of exudates in color fundus photographs is very important for early diagnosis of diabetic retinopathy. Methods We use deep convolutional neural networks for exudate detection. In order to incorporate high level anatomical knowledge about potential exudate locations, output of the convolutional neural network is combined with the output of the optic disc detection and vessel detection procedures. Results In the validation step using a manually segmented image database we obtain a maximum F1 measure of 0.78. Conclusions As manually segmenting and counting exudate areas is a tedious task, having a reliable automated output, such as automated segmentation using convolutional neural networks in combination with other landmark detectors, is an important step in creating automated screening programs for early detection of diabetic retinopathy.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo

POVEZANOST RADA