Segmentation in Diabetic Retinopathy using Deeply-Supervised Multiscalar Attention.

Abstract

Diabetic Retinopathy (DR) is a progressive chronic eye disease that leads to irreversible blindness. Detection of DR at an early stage of the disease is crucial and requires proper detection of minute DR pathologies. A novel Deeply-Supervised Multiscale Attention U-Net (Mult-Attn-U-Net) is proposed for segmentation of different DR pathologies viz. Microaneurysms (MA), Hemorrhages (HE), Soft and Hard Exudates (SE and EX). A publicly available dataset (IDRiD) is used to evaluate the performance. Comparative study with four state-of-the-art models establishes its superiority. The best segmentation accuracy obtained by the model for MA, HE, SE are 0.65, 0.70, 0.72, respectively.