Fully Convolutional Neural Network for Semantic Segmentation of Anatomical Structure and Pathologies in Colour Fundus Images Associated with Diabetic Retinopathy Article Swipe

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Oindrila Saha , Rachana Sathish , Debdoot Sheet · YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1902.03122

Diabetic retinopathy (DR) is the most common form of diabetic eye disease. Retinopathy can affect all diabetic patients and becomes particularly dangerous, increasing the risk of blindness, if it is left untreated. The success rate of its curability solemnly depends on diagnosis at an early stage. The development of automated computer aided disease diagnosis tools could help in faster detection of symptoms with a wider reach and reasonable cost. This paper proposes a method for the automated segmentation of retinal lesions and optic disk in fundus images using a deep fully convolutional neural network for semantic segmentation. This trainable segmentation pipeline consists of an encoder network, a corresponding decoder network followed by pixel-wise classification to segment microaneurysms, hemorrhages, hard exudates, soft exudates, optic disk from background. The network was trained using Binary cross entropy criterion with Sigmoid as the last layer, while during an additional SoftMax layer was used for boosting response of single class. The performance of the proposed method is evaluated using sensitivity, positive prediction value (PPV) and accuracy as the metrices. Further, the position of the Optic disk is localised using the segmented output map.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/1902.03122

PDF

https://arxiv.org/pdf/1902.03122

OA Status

green

Cited By

3

References

7

Related Works

20

OpenAlex ID

https://openalex.org/W2919616934

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OpenAlex ID

https://openalex.org/W2919616934

Canonical identifier for this work in OpenAlex

DOI

https://doi.org/10.48550/arxiv.1902.03122

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Title

Fully Convolutional Neural Network for Semantic Segmentation of Anatomical Structure and Pathologies in Colour Fundus Images Associated with Diabetic Retinopathy

Work title

Type

preprint

OpenAlex work type

Language

en

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Publication year

2019

Year of publication

Publication date

2019-02-07

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Authors

Oindrila Saha, Rachana Sathish, Debdoot Sheet

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Landing page

https://arxiv.org/abs/1902.03122

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PDF URL

https://arxiv.org/pdf/1902.03122

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Open access

Yes

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OA status

green

Open access status per OpenAlex

OA URL

https://arxiv.org/pdf/1902.03122

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Concepts

Softmax function, Artificial intelligence, Convolutional neural network, Segmentation, Computer science, Diabetic retinopathy, Fundus (uterus), Pattern recognition (psychology), Optic disk, Retinopathy, Optic disc, Binary classification, Computer vision, Optic nerve, Ophthalmology, Medicine, Retinal, Support vector machine, Diabetes mellitus, Endocrinology

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Cited by

3

Total citation count in OpenAlex

Citations by year (recent)

2021: 1, 2020: 2

Per-year citation counts (last 5 years)

References (count)

7

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Related works (count)

20

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