Self-Improved Beluga whale optimization algorithm based Optimal Trained Deep Maxout Model for Glaucoma Detection

Glaucoma is a fast-growing retinal disease, which is a chronic and neurodegenerative disease that affects patients progressively. The neuro-retinal nerve called the optic nerve, which connects the eye to the brain damages the eye due to lack of proper diagnosis. To diagnose this disease, ophthalmologists need to perform various tests. However, taking more tests requires a lot of time and that is expensive. Also, the early stage of glaucoma may not show any vision loss to the affected people. For detecting glaucoma, this paper intends to propose the glaucoma detection model that follows four phases including preprocessing, segmentation, Feature extraction, and glaucoma detection. In the Preprocessing phase, the retinal images are processed by median filtering. Subsequently, subjecting the preprocessed image to the segmentation, where the optic cup will be segmented by the improved U-net model. Subsequently, the features such as cup-to-disc ratio, inferior superior nasal temporal features, fractal features, and proposed Local Gabor features are derived from the segmented image. These extracted features are trained to the optimized DeepMaxout classifier for detection purposes. Finally, a novel self-improved Beluga whale optimization algorithm is proposed for the optimal training of DMN by tuning its optimal weights.