Optimized glaucoma detection using HCCNN with PSO-driven hyperparameter tuning.

. This study is focused on creating an effective glaucoma detection system employing a Hybrid Centric Convolutional Neural Network (HCCNN) model. By using Particle Swarm Optimization (PSO), classification accuracy is increased and computing complexity is reduced. Modified U-Net is also used to segment the optic disc (OD) and optic cup (OC) regions of classified glaucoma images to determine the severity of glaucoma.. The proposed HCCNN model can extract features from fundus images that show signs of glaucoma. To improve the model performance, hyperparameters like dropout rate, learning rate, and the number of neurons in the dense layer are optimized using the PSO method. The PSO algorithm iteratively assesses and modifies these parameters to minimize classification error. The classified glaucoma image is subjected to channel separation to enhance the visibility of relevant features. This channel-separated image is segmented using the modified U-Net to delineate the OC and OD regions.. Experimental findings indicate that the PSO-HCCNN model attains classification accuracy of 94% and 97% on DRISHTI-GS and RIM-ONE datasets. Performance criteria including accuracy, sensitivity, specificity, and AUC are employed to assess the system's efficacy, demonstrating a notable enhancement in the early detection rates of glaucoma. To evaluate the segmentation performance, parameters such as the Dice coefficient, and Jaccard index are computed.. The integration of PSO with the HCCNN model considerably enhances glaucoma detection from fundus images by optimizing essential parameters and accurate OD and OC segmentation, resulting in a robust and precise classification model. This method has potential uses in ophthalmology and may help physicians detect glaucoma early and accurately.