Application of an enhanced deep super-resolution network in retinal image analysis
Tanmay Gulati, Sourya Sengupta, Vasudevan Lakshminarayanan · 2020
Fundus imaging is widely used for the diagnosis of retinal diseases. Major ophthalmic diseases like glaucoma, diabetic retinopathy (DR), age-related macular degeneration (AMD) are diagnosed by examining retinal fundus images. Therefore, the efficient and reliable diagnosis largely depends upon the resolution of the images. In different diseased conditions, different pathologies and landmarks (haemorrhages, microaneurysms, exudates, blood vessels, optic disc and optic cup, fovea) of the retina get affected. In clinical situations it is often not possible to obtain good high-resolution images. Here, the techniques of super-resolution can be applied. The objective of super-resolution is to obtain a high-resolution image from a low-resolution input image. In this paper, we present results of the application of enhanced deep residual networks for single image super-resolution (EDSR) on retinal fundus images. This network is based on the SRResNet architecture involving skip connections. Using the public RIGA dataset, which consists of glaucoma and normal fundus images, we have trained the model using 2x, 4x and 8x scaling with three different optimizers each (namely ADAM, Stochastic Gradient Descent and RMSprop) to determine which optimizer is best for the different scales. We have also provided results obtained by varying the residual blocks in the network.