JJEM: Special Edition 2 (December 2024)
JJEM: Special Edition 2 (December 2024)
2024-12-08
Glaucoma Stage Detection Using CNN
Ruchitha G, Dr. Raghavendra S P
Glaucoma can cause their reparable visual loss if it is not Recognized and treated at an early stages. A degenerative disorder in the eyes is glaucoma. By merging image processing and deep learning techniques, the suggested work employs a CNN approach for glaucoma identification. By utilizing the features of both domains, the suggested effort seeks to increase the accuracy and efficacy in detection of glaucoma. Obtaining high-resolution retinal images is the initial phase in our technique, which is usually accomplished utilizing an optical coherence tomography (OCT) or fundus camera. In order to confirm that the collected information is best for study, image preprocessing techniques increase image quality, remove artifacts, and boost contrast. Different techniques for processing images enhance the visibility of relevant anatomical features.
Contrast-limited adaptive histogram equalization (CLAHE), Optical coherence tomography (OCT), Ocular cup (OC), Optic disc (OD), Convolutional Neural Networks (CNN), Optic nerve head (ONH)