Predicting diabetic retinopathy stage using Siamese Convolutional Neural Network

Abstract

ABSTRACT Diabetic retinopathy (DR) is considered to be a leading cause of blindness in people aged 16 to 64 years, affecting around 40% of the population diagnosed with diabetes mellitus (DM). DR is usually identified through retinal image analysis, and in some countries, such as Brazil, such diagnosis is hindered by limited access to specialized care, leading to lengthy waits for ophthalmological evaluations. This scenario makes the Brazilian Diabetic Society’s annual DR check recommendation impractical for many. To address this gap, our study introduces a novel Siamese Convolutional Neural Network (SCNN) for DR prediction, usable by primary care professionals. Our SCNN analyzes pairs of eye fundus images and employs shared weights in its layers to extract essential features, facilitating similarity measurement between neural network outputs. Despite challenges with limited and imbalanced datasets, our SCNN showed effectiveness. We tested it on four datasets (IDRiD, APTOS, Messidor-1, DIARETDB0), with accuracy ranging from 67.23% (APTOS) to 96.85% (DIARETDB0). Compared to other methods, our approach consistently excelled, especially in recall analysis. These results suggest that deep learning via Siamese networks is likely to be a viable and potential DR screening tool.