title: Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion.
abstract: 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 F1measure 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.
authors.affiliation: Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia. Electronic address: pavle.prentasic@fer.hr.
authors.forename: Pavle
authors.initials: P
authors.lastname: Prentašić
authors.affiliation: Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia.
authors.forename: Sven
authors.initials: S
authors.lastname: Lončarić
mesh_terms: D000465:Algorithms; D003930:Diabetic Retinopathy / Q000175:diagnosis*; D005122:Exudates and Transudates; D005654:Fundus Oculi; D006801:Humans; D016571:Neural Networks, Computer
keywords: Convolutional neural networks; Diabetic retinopathy; Exudates; Fundus photographs; Machine learning