Microaneurysm detection in fundus images using a two-step convolutional neural network.

Journal: Biomedical engineering online
Published Date: May 29, 2019

Abstract

BACKGROUND AND OBJECTIVES: Diabetic retinopathy (DR) is the leading cause of blindness worldwide, and therefore its early detection is important in order to reduce disease-related eye injuries. DR is diagnosed by inspecting fundus images. Since microaneurysms (MA) are one of the main symptoms of the disease, distinguishing this complication within the fundus images facilitates early DR detection. In this paper, an automatic analysis of retinal images using convolutional neural network (CNN) is presented.

Authors

  • Noushin Eftekhari
    Machine Vision Lab., Computer Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad (FUM), Azadi Sqr., Mashhad, Iran.
  • Hamid-Reza Pourreza
    Machine Vision Lab., Computer Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad (FUM), Azadi Sqr., Mashhad, Iran. hpourreza@um.ac.ir.
  • Mojtaba Masoudi
    Machine Vision Lab., Computer Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad (FUM), Azadi Sqr., Mashhad, Iran.
  • Kamaledin Ghiasi-Shirazi
    Machine Vision Lab., Computer Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad (FUM), Azadi Sqr., Mashhad, Iran.
  • Ehsan Saeedi
    Machine Vision Lab., Computer Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad (FUM), Azadi Sqr., Mashhad, Iran.

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