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.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence