Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

The intuitive fuzzy set has found important application in decision-making and machine learning. To enrich and utilize the intuitive fuzzy set, this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge. Retinal image detections are categorized as normal eye recognition, suspected glaucomatous eye recognition, and glaucomatous eye recognition. Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images. The proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network (CNN) and deep learning to identify the fuzzy weighted regularization between images. This methodology was used to clarify the input images and make them adequate for the process of glaucoma detection. The objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System (FES) and Fuzzy differential equation (FDE). The intensities of the different regions in the images and their respective peak levels were determined. Once the peak regions were identified, the recurrence relationships among those peaks were then measured. Image partitioning was done due to varying degrees of similar and dissimilar concentrations in the image. Similar and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and FDE. This distinguished between a normal and abnormal eye condition, thus detecting patients with glaucomatous eyes.