Basu, Sanhita ; Mitra, Sushmita (2021) Segmentation in Diabetic Retinopathy using Deeply-Supervised Multiscalar Attention In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 01-05 November 2021, Mexico.
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Official URL: https://doi.org/10.1109/EMBC46164.2021.9630600
Related URL: http://dx.doi.org/10.1109/EMBC46164.2021.9630600
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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Source: | Copyright of this article belongs to IEEE. |
| Keywords: | Training; Pathology; Image segmentation; Retinopathy; Biological system modeling; Blindness; Diabetes. |
| ID Code: | 140190 |
| Deposited On: | 07 Sep 2025 06:59 |
| Last Modified: | 07 Sep 2025 06:59 |
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