INTEGRATING DUAL FUNDUS IMAGE DEEP FEATURE POOLING BASED ON CUP AND DISC SEGMENTATION FOR EFFICIENT GLAUCOMA DETECTION FRAMEWORK INDULGING HEURISTIC CONCEPT

The permanent damage occurring to the optical nerve, which transmits the visualized images to the brain leading to permanent vision loss, is called glaucoma. The detection and diagnosis of glaucoma at the beginning stage are essential to prevent permanent damage caused to vision as there are no initial symptoms to detect the onset of the damage, and there is no cure while the damage progresses to its extreme stage. Many models utilizing deep learning technology have been executed to identify the presence of glaucoma using digital Fundus Images (FI). However, these methods need specialized hardware and are computationally complicated as there is not much-labeled data available, leading to generalization issues in these models. Therefore, the development of a deep learning-oriented model for Glaucoma Detection (GD) using the dual feature pooling of cup and disc segmentation features with the aid of the FI is the main motive behind this work. At first, the digital FI is congregated from standardized online data sources. Then the gathered images are considered for the image segmentation procedure. The disc is segmented from the input image with the aid of the Transformer based Adaptive ResUNet++ (TA-ResUNet++) framework. The segmented disc image is now provided to hybrid convolution networks (HCN) for extracting the disc-oriented features. At the same time, the cup is segmented from the gathered input image using the executed TA-ResUnet++. Following this, the cup features are extracted using the same HCN model. The obtained features regarding the disc and the cup are concatenated in order to generate a single pool of glaucoma features. Then these features are inputted into the implemented bidirectional long short-term memory with multi-head attention (Bi-LSTM-MHA) mechanism to detect the presence of glaucoma in people. The segmentation and detection outcomes are made better by optimizing the parameters in the TA-ResUNet++ and Bi-LSTM-MHA models with the aid of hybrid sewing training with the Chameleon swarm optimization algorithm (HST-CSA). The experimental analysis is done to analyze the effective performance of the developed GD model. Jaccard analysis performed for the developed HST-CSOA-TA-ResUNet++-based disc segmentation gained better performance as 18.29% than MobileNet, 14.11% efficient than ResNet, 12.79% superior to Bi-LSTM and 5.43% higher than Bi-LSTM-MHA while considering best as the statistical measure.