Automatic glaucoma detection from fundus images using transfer learning

Glaucoma is an eye disease that damages the optic nerve (or retina) and impairs vision. This disease can be prevented with regular checkups, but this increases the workload for professionals and the time it takes to get results. So an automated method using deep learning would be helpful for detection of disease. In order to shorten the diagnosis time for glaucoma, this paper proposed a deep learning based method for automatic glaucoma detection. The experiments are conducted on glaucoma datasets available on Kaggle. This paper used transfer learning based pretrained models namely DenseNet169, MobileNet, InceptionV3, Xception, ReseNet152V2,and VGG19. Among all models DenseNet169 gives best result with accuracy 0.993590 and precision and recall of 0.993671 and 0.9935895 respectively. A comparison of the best model results with existing work shows that the proposed model provides better results.