AN INTERPRETABLE GLAUCOMA DETECTION USING DUAL SCALE CROSS-ATTENTION VISION TRANSFORMER-BASED LONG SHORT TERM MEMORY WITH OPTICAL CUP AND DISK SEGMENTATION

Glaucoma is a kind of eye disease that tends to generate harm to the optic nerve. It is a neurodegenerative illness, which develops intraocular hypertension because of its maximized aqueous humor and blockage between the cornea and iris. It causes destruction to the optic nerve head, which transfers visual stimulus to the brain from the eyes. This results in loss of visual field and blindness. For vision, glaucoma is known to be a sneak thief due to its complexity in detecting it in the early stage. It requires continuous screening to determine the neurological disorder. Effective identification of glaucoma requires more cost and time, but it also causes human error in the detection phase based on resource availability. The problems based on the robustness of the algorithm are not solved in the earlier method especially relative to that human expert counterpart. Therefore, effective glaucoma detection with the help of deep learning is developed to recognize eye disease in the early stage. At first, the input eye images are taken from the available sites. Subsequently, the procedure for segmentation is done using the Optimized Dilated Mobile-Unet++ (ODMUnet++) to segment the optic disc and optic cup in the input images. Here, the parameters in the developed ODMUnet++ are optimized using an Improved Drawer Algorithm (IDrA). The segmented "optic disc and optic cup" images are given to the developed Dual Scale Cross-Attention Vision Transformer-based Long Short-Term Memory (DSCAViT-LSTM) for glaucoma detection. The experimental outcomes of the recommended model are evaluated with other deep learning techniques to ensure its efficacy.