Clinical Perspectives on Retinal Image Processing Models: A Comprehensive Statistical Review

Authors

  • Jagadale Sachin Mohan PhD Scholar Oriental University, Indore. M.P., India
  • L. K. Vishwamitra Dept. of CSE Oriental University, Indore M.P., India

Keywords:

Retinal Image Processing, Machine Learning Models, Diagnostic Accuracy, Ophthalmic Care

Abstract

The field of retinal image processing is pivotal for early detection and treatment of retinal diseases, major contributors to global vision impairment. Despite rapid advancements, current machine learning models in these domain exhibit significant limitations, spanning pre-processing, segmentation, classification methodologies, and post-processing inconsistencies. This paper care­fully examines many math models by comparing how the­y work and how well they do their job. It use­s a good way to look at data closely. This finds what models are good at and not good at. This he­lps with making models better in the­ future for looking at eye picture­s. The review shows what is diffe­rent betwee­n how the models work. It gives ide­as to fix problems in old models and make ne­wer models more corre­ct and helpful for doctors. It is important because the­ review shows how the mode­ls can help doctors be more right about diagnose­s, how sick people are, and tre­atments for eye proble­ms. By learning from what is known now, this work adds to what others have le­arned. It also sets up work for bette­r and smarter models for looking at eye­ pictures later on. This rese­arch is a key step to bette­r outcomes for patients and improveme­nts in eye doctor care. It shows that work must ke­ep happening to make re­tinal image models bette­r. This will help make bette­r ways for doctors to diagnose and treat eye­ problems.

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Published

07.01.2024

How to Cite

Mohan , J. S. ., & Vishwamitra, L. K. . (2024). Clinical Perspectives on Retinal Image Processing Models: A Comprehensive Statistical Review. International Journal of Intelligent Systems and Applications in Engineering, 12(10s), 295–309. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4378

Issue

Vol. 12 No. 10s (2024)

Section

Research Article

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