Automatic detection of papilledema through fundus retinal images using deep learning

Papilledema is a syndrome of the retina in which retinal optic nerve is inflated by elevation of intracranial pressure. The papilledema abnormalities such as retinal nerve fiber layer (RNFL) opacification may lead to blindness. These abnormalities could be seen through capturing of retinal images by means of fundus camera. This paper presents a deep learning-based automated system that detects and grades the papilledema through U-Net and Dense-Net architectures. The proposed approach has two main stages. First, optic disc and its surrounding area in fundus retinal image are localized and cropped for input to Dense-Net which classifies the optic disc as papilledema or normal. Second, consists of preprocessing of Dense-Net classified papilledema fundus image by Gabor filter. The preprocessed papilledema image is input to U-Net to achieve the segmented vascular network from which the vessel discontinuity index (VDI) and vessel discontinuity index to disc proximity (VDIP) are calculated for grading of papilledema. The VDI and VDIP are standard parameter to check the severity and grading of papilledema. The proposed system is evaluated on 60 papilledema and 40 normal fundus images taken from STARE dataset. The experimental results for classification of papilledema through Dense-Net are much better in terms of sensitivity 98.63%, specificity 97.83%, and accuracy 99.17%. Similarly, the grading results for mild and severe papilledema classification through U-Net are also much better in terms of sensitivity 99.82%, specificity 98.65%, and accuracy 99.89%. The deep learning-based automated detection and grading of papilledema for clinical purposes is first effort in state of art.