Optic disc and cup segmentation methods for glaucoma detection using twin- inception transformer hinge attention network with cycle consistent convolutional neural network

Abstract

One of the primary sources of blindness worldwide is glaucoma and can only be treated if detected early. This study’s goal is to design a comprehensive scheme for the glaucoma classification incorporating advanced approaches for extracting attributes and segmentation. To begin with, the optic disc and cup are well segmented from the retinal pictures with the Pufferfish Optimization Algorithm (POA). Due to POA, it becomes very easy to more accurately define the area of the optic disc and cup which in turn helps in glaucoma diagnosis depending on the severity. Joining the state-of-the-art neural network designs for attributes extraction and categorization, a new hybrid deep learning (DL) method is described. In the developed model, the Primary Inception Transformer, Hinge Attention Network, and Cycle-Consistent Convolutional Neural Network (Cycle-Consistent CNN) are in fusion with the Human Memory Optimization Algorithm (HMOA). The Twin-Inception Transformer captures intricate spatial interactions in retinal images by utilizing transformer processes, while the Hinge Attention Network fortifies feature learning by a dynamic attention model. In incurred to enhance the training process, HMOA replicates the human memory consolidation process to increase the trainees’ retention and reliability. This combined approach enhances the model’s capability of generalization while still preserving the highest quality of features extracted. The usefulness of the indicated architecture has been proved in experiments using the freely available glaucoma datasets. When compared with today’s benchmark techniques the presented work yields a better performance such as 99.7% accuracy, and 99.5% precision.