Volume 18, No. 6, 2021
The Automatic Diagnosis Systems For Detecting Glaucoma In Fundus Images Using Fusion GLCM And CNN Techniques
K.Subha , Dr. S.Kother Mohideen
Abstract
The automated detection of optical disc in the retinal fundus images is gaining more attention by ophthalmologists. Optical disc detection plays a crucial role in retinal image processing for the identification of various fundus structures and eye disorders. The proposed study first determines how human perception functions for optical disc detection using a bottom-up visual focus paradigm since human visual vision has not been extensively studied for optical disc detection and an attempt to acquire data analytics based on eyes. Accurate early identification could prevent eyesight problems. Glaucoma can be identified using several categorization techniques, and the severity of the condition is evaluated in retinal images. Novel computations are developed to distinguish between and categorise the different stages of glaucoma. The affectability, specificity, and exactness of the execution measurements need to be examined. The precision of our method was 95.81%. Additionally, it is anticipated that the proposed developed algorithm will aid doctors in early diagnosis of diabetic retinopathy.
Pages: 6896-6911
Keywords: Glaucoma detection, optic disc, Pre-trained CNNs and Classification.