• G.V. Vinod
• S. Ravi NagaDurgaRao
• A.Srinaja
• L.Hemanth Naidu
• D.SanthuPriya

Ophthalmologists often consider the health of the eye's vascular system when making diagnoses. In order to analyse disorders of the retinal vasculature, this project suggests analysing retinal images with an emphasis on the accurate identification of arteries and exudates. Retinal blood vessel comparisons play a crucial role in the early diagnosis of various illnesses. Retinal disorders such as diabetic macular edoema, drusen, and choroidal neovascularization may be detected with the use of a Deep learning model trained using the Convolution Neural Network (CNN) method. Retinal tissue may be imaged quickly and precisely using optical coherence tomography (OCT), a sort of image processing. To detect the retinal disorder, we employ the Keras deep learning framework. The most important result of this work is a method for diagnosing optical disorders from a photograph of the retina. Furthermore, the model may be represented in a web application by integrating it with the python flask framework.

Deep Learnin,CNN,OCT,RNN

"DEEP LEARNING MODEL FOR OPTICAL DIAGNOSIS OF AN RETINA ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 2, page no.c521-c527, February-2023, Available :http://www.jetir.org/papers/JETIR2302271.pdf

"DEEP LEARNING MODEL FOR OPTICAL DIAGNOSIS OF AN RETINA ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 2, page no. ppc521-c527, February-2023, Available at : http://www.jetir.org/papers/JETIR2302271.pdf