Authors : Sabitha S; Sahana K.N.; Sandhya S.; Theertha Sukumaran

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/54mjmvyn

Scribd : https://tinyurl.com/bddf7vkb

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY551

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The project gives the people an insight of how fundus image processing can be used for identifying various human disease. A review of human diseases that can be diagnosed using fundus image is done. The changes in eyes especially the retina acts as the objective measure which captures the change in cell using which the detection is performed. The aim of this project is to show the importance of retinol images in finding various human disorders. Retinol is nothing but a derivative of vitamin A which plays a crucial role in human body like vision, growth regulation etc. Changes in the level of retinol can cause diseases in human. The severity of disease may range from simple metabolic disorders to dangerous cardiovascular disease. The development in technology has enabled us to use fundus images in finding diseases like Diabetic retinopathy, Glaucoma, Age macular degeneration and cardiovascular diseases without involving the medical experts directly.

Keywords : Fundus Image, Retinol, Diabetic Retinopathy, Gluacoma, Age Macular Degeneration, Cardiovascular Disease.

References :

1. Supriya Mishra, Zia saquib, seema. Diabetic Retionopathy Detection using Deep Learning. 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE). https://ieeexplore.ieee.org/document/9277506.
2. Satwik, Bhargav Patil, Shradul. Deep learning approach for diabetic retinopathy detection Using transfer learning. 2020 IEEE International Conference for Innovationin Technology (INOCON).
3. Piumi Liyana, raviru Jayathilake. Automatic diagnosis of diabetic retinopathy using machine learning. 2020 5th International Conference on Information Technology Research (ICITR) https://ieeexplore.ieee.org/document/9310818
4. Mayuresh,Sonali, gaitonve, amudha. Detection of diabetic retinopathy and its classification from fundus images. 2021 International Conference on Computer Communication and Informatics (ICCCI).https://ieeexplore.ieee.org/document/9402347 
5. Mini Yadav, Raghav goyale, Rajeshwari. Deep learning-based DR detection from retinal images. 2021 International Conference on Intelligent Technologies(CONIT).https://ieeexplore.ieee.org/document/9733545
6. Karthik N hari, Karthik AN, M.rajashekar. DR detection with feature enhancement and deep learning. 2021 International Conference on System, Computation, Automation and Networking (ICSCAN). https://ieeexplore.ieee.org/document/9526438
7. Shariya, Farjana Kabir, Pintu. Diagnosis of diabetic retinopathy using deep learning technique. 2021 5th International Conference on Electrical Information and Communication Technology (EICT). https: // ieeexplore.ieee.org/document/10060706
8. MS Sowmya, S Santhosh. Diabetic retinopathy recognition using CNN. 2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC). https:// ieeexplore. ieee.org / document/10206262
9. Youcef Brik, Bilal, Ishaq, Hanine. Deep learning-based framework for automatic diabetic retinopathy detection. 2022 32nd International Conference on Computer Theory and Applications (ICCTA). https://ieeexplore.ieee.org/document/10110238
10. Kaustuvh ratna, Akash, Raghav, agal. Deep learning approach for detection of diabetic retinopathy. IEEE Technologies(DICCT).https://ieeexplore.ieee.org/document/9498502.