Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network
| Autor: | Jen Hong Tan, U. Rajendra Acharya, Hamido Fujita, A. Krishna Rao, Sulatha V. Bhandary, Kuang Chua Chua, Sobha Sivaprasad |
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| Rok vydání: | 2017 |
| Předmět: |
Information Systems and Management
Computer science Fundus image Automated segmentation 02 engineering and technology Fundus (eye) Convolutional neural network 030218 nuclear medicine & medical imaging Theoretical Computer Science 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine Artificial Intelligence 0202 electrical engineering electronic engineering information engineering medicine Segmentation Computer vision Blindness business.industry Retinal Diabetic retinopathy medicine.disease Computer Science Applications chemistry Control and Systems Engineering 020201 artificial intelligence & image processing Artificial intelligence business Software |
| Zdroj: | Information Sciences. 420:66-76 |
| ISSN: | 0020-0255 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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