Fuzzy acceptance Analysis of Impact of Glaucoma and Diabetic Retinopathy using Confusion Matrix

• Published in: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) pp. 1 - 5
• Main Authors: D, Faridha Banu, Sindhwani, Nidhi, G, Sasi, A, Kaleel Rahuman, S, Meivel
• Format: Conference Proceeding
• Language: English
• Published: IEEE 13.10.2022
• Subjects: Blindness; Confusion matrix; Deep Learning; Diabetes; Diabetic Retinopathy; Exudates; Fuzzy acceptance value; Glaucoma; Image segmentation; Retina; Retinopathy; Visualization
• DOI: 10.1109/ICRITO56286.2022.9964858

Diabetic retinopathy is a vision loss and blindness disorder caused by diabetes. It affects the retina's blood vessels. The most prevalent retinal illnesses are glaucoma and diabetic retinopathy (DR) both of which are important causes of blindness. In addition to causing diabetic retinopathy (DR), longterm hyperglycemia also produces retinal lesions, exudates, microaneurysms, and haemorrhages that can result in blindness. Diabetes mellitus affects [1] an estimated 210 million people worldwide, with 10-18% having or developing the disease. Therefore, accurate and prompt DR identification is essential to avoiding DR and potential visual loss. Glaucoma is frequently associated with increased vitreous fluid pressure in the eye, but this is not always the case. Glaucoma is thought to be the world's second leading cause of blindness, after cataract. This result shows that fuzzy acceptance analysis of glaucoma and Diabetic retinopathy [2].