This technology offers an automated diagnostic solution for retinal health based on fundus image and deep learning technology. The network automatically classifies fundus images of age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma and normal into abnormal and normal classes. The network also can be run on any computing platform, delivering instant result for clinicians and patients
TECHNOLOGY FEATURES & SPECIFICATIONS
The developed 10-layered neural network is able to automatically classify images of age-related macular degeneration (AMD), diabetic retinopathy (DR) and glaucoma as abnormal and images of normal subjects as normal. The input image for the system is of size 180 x 270 pixels. The network uses different sized kernels to interpret the input fundus image, thereafter the feature maps are being concatenated for analysis.
The system was developed and tested on a total of 2986 images (collecting from various sources). ‘ADAM’ optimizer was used to train the net, and achieved an accuracy of 95.24% on a set of 1492 images. The sensitivity and specificity was 91.67% and 96.81% respectively. The developed network is commercially ready for deployment to any computing or mobile devices.
This automated diagnosis solution can be deployed at any clinical facility for the mass screening and routine screening of the fundus such as in eye clinics, polyclinics or community centre. The A I system is able to automatically diagnose for normal or abnormal cases and make referrals to eye hospitals for the necessary follo up and treatment by eye specialist
•The diagnosis is fast and reliable.
•Reduce clinician’s workload.
•Network is compact (small). Readily to be deployed on any computing or mobile devices.