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 journalArticlepeer-review

230 Citations (Scopus)


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
Publication statusPublished - 01-12-2017

All Science Journal Classification (ASJC) codes

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


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