Optimized convolutional neural network for glaucoma detection with improved optic-cup segmentation

Abstract

Glaucoma is the foremost cause of permanent loss of visioahn; it develops slowly with no discernible sign. Early glaucoma recognition is critical because it might aid to slow the progression of the disease. Customary schemes are less precise and manual. As a result, automatic glaucoma analysis is required to identify glaucoma at the earliest with greater precision. The purpose here is to bring in a new scheme in which pre-processing is done using Gaussian filtering, which aids in the removal of unwanted noise in images. Then Optic Cup segmentation is performed using the Modified Level Set Algorithm. Followed by segmentation, the morphological features (disc area, cup area, and blood vessel), as well as non-morphological features (Color, Shape, and Modified LBP), are derived. The Blood vessel thickness is 5 to 100 micrometers. These features are then classified using the Optimized CNN framework, where the weights get optimized via the Self Adaptive Butterfly Optimization Algorithm (SA-BOA). The precision of the developed approach was 21.16%, 7.35%, 6.62%, 2.98%, 4.29%, 3.89%, 5.67%, 6.23%, 6.79%, and 1.63% better than the values obtained for conservative techniques Similarly, the adopted model's negative metrics show negligible values when compared to other models. Thus, the proposed method supremacy was validated successfully.