Detection of Glaucoma from Fundus Images Using Novel Evolutionary-Based Deep Neural Network

Glaucoma is an asymptotic condition that damages the optic nerves of a human eye. Glaucoma is frequently caused due to abnormally high pressure in an eye that leads to permanent blindness. Detecting glaucoma at an initial phase has the possibility of curing this disease, but diagnosing accurately is considered as a challenging task. Therefore, this paper proposes a novel method known as a glaucoma detection system that performs the diagnosis of glaucoma by exploiting the prescribed characteristics. The significant intention of this paper involves diagnosing the glaucoma disease present at the top optical nerve of a human eye. The proposed glaucoma detection has used four different phases namely data preprocessing or enhancement phase, segmentation phase, feature extraction phase, and classification phase. Here, a novel classifier named fractional gravitational search-based hybrid deep neural network (FGSA-HDNN) is developed for the effective classification of glaucoma-infected images from the normal image. Finally, the experimental analysis for the proposed approach and various other techniques are performed, and the accuracy rate while diagnosing glaucoma achieved is 98.75%.