cruznaranjo-diabeticretinopathy-2021.pdf (1.41 MB)
Diabetic Retinopathy Improved Detection Using Deep Learning
journal contribution
posted on 2021-01-01, 00:00 authored by Angel Ayala, Tomas Ortiz Figueroa, Bruno Fernandes, Francisco CruzDiabetes 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.
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Journal
Applied Sciences-BaselVolume
11Issue
24Article number
11970Pagination
1 - 11Publisher
MDPILocation
Basel, SwitzerlandPublisher DOI
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2076-3417eISSN
2076-3417Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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