Algorithm-based diagnostic application for diabetic retinopathy
  detection
Agnieszka Cisek Karolina Korycinska Leszek Pyziak Marzena Malicka Tomasz Wiecek Grzegorz Gruzel Kamil Szmuc Jozef Cebulski Mariusz Spyra
Diabetic retinopathy (DR) is a growing health problem worldwide and is a
leading cause of visual impairment and blindness, especially among working
people aged 20-65. Its incidence is increasing along with the number of
diabetes cases, and it is more common in developed countries than in developing
countries. Recent research in the field of diabetic retinopathy diagnosis is
using advanced technologies, such as analysis of images obtained by
ophthalmoscopy. Automatic methods for analyzing eye images based on neural
networks, deep learning and image analysis algorithms can improve the
efficiency of diagnosis. This paper describes an automatic DR diagnosis method
that includes processing and analysis of ophthalmoscopic images of the eye. It
uses morphological algorithms to identify the optic disc and lesions
characteristic of DR, such as microaneurysms, hemorrhages and exudates.
Automated DR diagnosis has the potential to improve the efficiency of early
detection of this disease and contribute to reducing the number of cases of
diabetes-related visual impairment. The final step was to create an application
with a graphical user interface that allowed retinal images taken at
cooperating ophthalmology offices to be uploaded to the server. These images
were then analyzed using a developed algorithm to make a diagnosis.