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Diabetic Retinopathy Improved Detection Using Deep Learning

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journal contribution
posted on 01.01.2021, 00:00 authored by Angel Ayala, Tomas Ortiz Figueroa, Bruno Fernandes, Francisco CruzFrancisco Cruz
Diabetes is a disease that occurs when the body presents an uncontrolled level of glucose that is capable of damaging the retina, leading to permanent damage of the eyes or vision loss. When diabetes affects the eyes, it is known as diabetic retinopathy, which became a global medical problem among elderly people. The fundus oculi technique involves observing the eyeball to diagnose or check the pathology evolution. In this work, we implement a convolutional neural network model to process a fundus oculi image to recognize the eyeball structure and determine the presence of diabetic retinopathy. The model’s parameters are optimized using the transfer-learning methodology for mapping an image with the corresponding label. The model training and testing are performed with a dataset of medical fundus oculi images and a pathology severity scale present in the eyeball as labels. The severity scale separates the images into five classes, from a healthy eyeball to a proliferative diabetic retinopathy presence. The latter is probably a blind patient. Our proposal presented an accuracy of 97.78%, allowing for the confident prediction of diabetic retinopathy in fundus oculi images.

History

Journal

Applied Sciences-Basel

Volume

11

Issue

24

Article number

11970

Pagination

1 - 11

Publisher

MDPI

Location

Basel, Switzerland

ISSN

2076-3417

eISSN

2076-3417

Language

eng

Publication classification

C1 Refereed article in a scholarly journal