id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
cana-1835	Anand Rajasekaran	Image Classification with Hypertensive Angle Disease Detection with Geometric Local Derivative Pre-Processing 	2024	17	.pdf	application/pdf	6977	268	33	Sample retinal image Glaucoma detection accuracy (%) ASM-HSML MFV-DBN CLAHE 500 96 94 91 1000 94.55 91.85 89.15 1500 91.35 89.15 88.55 2000 90.85 88.35 86.25 2500 89.35 87.15 85.45 3000 89 85 83.15 3500 88.45 84.35 82 4000 88 82.15 81.15 4500 87.35 81.55 80 500 87.15 80 79.55 Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 2 (2025) 606 https://internationalpubls.com Figure 5 Accuracy of glaucoma detection compared to a sample retinal picture In accordance with the experimental sample retina images, which have been separated into four categories and categorized as being either diseased as well as healthy using a randomization individual Identification, Figure 5 above illustrates the correctness of ophthalmology detection. In the interest of recognizing ophthalmology, an evaluation of retinal images and optical cupping segment and classification methods was developed in [9].	cache/cana-1835.pdf	txt/cana-1835.txt