Pre-diagnosis of diabetic retinopathy implementing supervised learning algorithms using an ocular fundus Latin-American dataset for cross-data validation

Abstract

Nowadays diabetes is a disease with worldwide presence and high mortality rate, causing a big social and economic impact. One of the major negative effects of diabetes is visual loss due to diabetic retinopathy (DR). To prevent this condition is necessary to identify referable patients by screening for DR, and complementing with an Optic Coherence Tomography (OCT), that is another study to perform an early detection of blindness doing several longitudinal scans at a series of lateral locations to generate a map of reflection sites in the sample and display it as a two-dimensional image achieving transmission images in turbid tissue. Regrettably the number of ophthalmologists and OCT devices is not enough to provide an adequate health care to the diabetic population. Although there exist AI systems capable of do DR screening, they do not aim the assessment specifically in macula area considering visible and proliferated anomalies, signs of high damage and late intervention. This work presents three surpevised machine learnig algorithms; a Random Forest (RF) classifier, a Convolutional Neural Network (CNN) model, and a transfer learning (TL) pretrained model able to sort fundus images in three classes as an fundus images exclusive database is labeled. Processing techniques such as channel splitting, color space transforms, histogram and spatial based filters and data augmentation are used in order to detect presence of diabetic retinopathy. The stages of this work are: Publicly available dataset debugging, macular segmentation and cropping, data pre-processing, features extraction, model training, test and validation performance evaluation with a exclusive Latin-American dataset considering accuracy, sensitivity and specificity as metrics. The best results achieved are a 61.22% of accuracy, 86.67% of sensitivity and 89.47% of specificity.