Publication

Abstract : Glaucoma is a type of visual impairment that is caused due to damage in the optic nerve. The vision loss increases from the peripheral vision towards the central vision, leading to blindness if untreated. The proposed approach is a Computer-Aided Detection (CADe) system using deep learning to screen visual field loss in glaucoma patients while performing different day-to-day activities such as searching objects, viewing photographs, etc. Incorporating an eye-tracking device helps to identify eye movements of glaucoma patients while performing different activities. Different day-to-day activities are depicted in the form of visual exploration tasks. CADe system fuses performance parameters and eye gaze parameters during visual exploration tasks onto images, to guide health care professionals of primary eye care centers in glaucoma screening. The pertinent eye gaze and performance parameters are visualized in the form of three fusion maps: Gaze Fusion Map (GFM), Gaze Fusion Reaction Time (GFRT) map, Gaze Convex Hull Map (GCHM), which are the outcomes of different visual exploration tasks. In addition, the explainability techniques applied in CADe generated Gaze Exploration - index (GE-i) that discriminates glaucoma and normal.