Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network

Jen Hong Tan, Hamido Fujita, Sobha Sivaprasad, Sulatha V. Bhandary, A. Krishna Rao, Kuang Chua Chua, U. Rajendra Acharya

Research output: Contribution to journalArticle

46 Citations (Scopus)

Abstract

Screening for vision threatening diabetic retinopathy by grading digital retinal images reduces the risk of blindness in people with diabetes. Computer-aided diagnosis can aid human graders to cope with this mounting problem. We propose to use a 10-layer convolutional neural network to automatically, simultaneously segment and discriminate exudates, haemorrhages and micro-aneurysms. Input image is normalized before segmentation. The net is trained in two stages to improve performance. On average, our net on 30,275,903 effective points achieved a sensitivity of 0.8758 and 0.7158 for exudates and dark lesions on the CLEOPATRA database. It also achieved a sensitivity of 0.6257 and 0.4606 for haemorrhages and micro-aneurysms. This study shows that it is possible to get a single convolutional neural network to segment these pathological features on a wide range of fundus images with reasonable accuracy.

Original languageEnglish
Pages (from-to)66-76
Number of pages11
JournalInformation Sciences
Volume420
DOIs
Publication statusPublished - 01-12-2017

Fingerprint

Aneurysm
Segmentation
Neural Networks
Neural networks
Computer aided diagnosis
Computer-aided Diagnosis
Diabetes
Grading
Medical problems
Mountings
Digital Image
Screening
Range of data
Vision
Human

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

Cite this

Tan, Jen Hong ; Fujita, Hamido ; Sivaprasad, Sobha ; Bhandary, Sulatha V. ; Rao, A. Krishna ; Chua, Kuang Chua ; Acharya, U. Rajendra. / Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. In: Information Sciences. 2017 ; Vol. 420. pp. 66-76.
@article{39a23b19e849424baec0d846a3e4a88c,
title = "Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network",
abstract = "Screening for vision threatening diabetic retinopathy by grading digital retinal images reduces the risk of blindness in people with diabetes. Computer-aided diagnosis can aid human graders to cope with this mounting problem. We propose to use a 10-layer convolutional neural network to automatically, simultaneously segment and discriminate exudates, haemorrhages and micro-aneurysms. Input image is normalized before segmentation. The net is trained in two stages to improve performance. On average, our net on 30,275,903 effective points achieved a sensitivity of 0.8758 and 0.7158 for exudates and dark lesions on the CLEOPATRA database. It also achieved a sensitivity of 0.6257 and 0.4606 for haemorrhages and micro-aneurysms. This study shows that it is possible to get a single convolutional neural network to segment these pathological features on a wide range of fundus images with reasonable accuracy.",
author = "Tan, {Jen Hong} and Hamido Fujita and Sobha Sivaprasad and Bhandary, {Sulatha V.} and Rao, {A. Krishna} and Chua, {Kuang Chua} and Acharya, {U. Rajendra}",
year = "2017",
month = "12",
day = "1",
doi = "10.1016/j.ins.2017.08.050",
language = "English",
volume = "420",
pages = "66--76",
journal = "Information Sciences",
issn = "0020-0255",
publisher = "Elsevier Inc.",

}

Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. / Tan, Jen Hong; Fujita, Hamido; Sivaprasad, Sobha; Bhandary, Sulatha V.; Rao, A. Krishna; Chua, Kuang Chua; Acharya, U. Rajendra.

In: Information Sciences, Vol. 420, 01.12.2017, p. 66-76.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network

AU - Tan, Jen Hong

AU - Fujita, Hamido

AU - Sivaprasad, Sobha

AU - Bhandary, Sulatha V.

AU - Rao, A. Krishna

AU - Chua, Kuang Chua

AU - Acharya, U. Rajendra

PY - 2017/12/1

Y1 - 2017/12/1

N2 - Screening for vision threatening diabetic retinopathy by grading digital retinal images reduces the risk of blindness in people with diabetes. Computer-aided diagnosis can aid human graders to cope with this mounting problem. We propose to use a 10-layer convolutional neural network to automatically, simultaneously segment and discriminate exudates, haemorrhages and micro-aneurysms. Input image is normalized before segmentation. The net is trained in two stages to improve performance. On average, our net on 30,275,903 effective points achieved a sensitivity of 0.8758 and 0.7158 for exudates and dark lesions on the CLEOPATRA database. It also achieved a sensitivity of 0.6257 and 0.4606 for haemorrhages and micro-aneurysms. This study shows that it is possible to get a single convolutional neural network to segment these pathological features on a wide range of fundus images with reasonable accuracy.

AB - Screening for vision threatening diabetic retinopathy by grading digital retinal images reduces the risk of blindness in people with diabetes. Computer-aided diagnosis can aid human graders to cope with this mounting problem. We propose to use a 10-layer convolutional neural network to automatically, simultaneously segment and discriminate exudates, haemorrhages and micro-aneurysms. Input image is normalized before segmentation. The net is trained in two stages to improve performance. On average, our net on 30,275,903 effective points achieved a sensitivity of 0.8758 and 0.7158 for exudates and dark lesions on the CLEOPATRA database. It also achieved a sensitivity of 0.6257 and 0.4606 for haemorrhages and micro-aneurysms. This study shows that it is possible to get a single convolutional neural network to segment these pathological features on a wide range of fundus images with reasonable accuracy.

UR - http://www.scopus.com/inward/record.url?scp=85027565220&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85027565220&partnerID=8YFLogxK

U2 - 10.1016/j.ins.2017.08.050

DO - 10.1016/j.ins.2017.08.050

M3 - Article

AN - SCOPUS:85027565220

VL - 420

SP - 66

EP - 76

JO - Information Sciences

JF - Information Sciences

SN - 0020-0255

ER -