CFP last date
20 December 2024
Reseach Article

Diagnosis of Diabetic Retinopathy using Blob Analysis

by Aeman Alijahan Patel, Rachna Y. Patil
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 32
Year of Publication: 2021
Authors: Aeman Alijahan Patel, Rachna Y. Patil
10.5120/ijca2021921707

Aeman Alijahan Patel, Rachna Y. Patil . Diagnosis of Diabetic Retinopathy using Blob Analysis. International Journal of Computer Applications. 183, 32 ( Oct 2021), 8-11. DOI=10.5120/ijca2021921707

@article{ 10.5120/ijca2021921707,
author = { Aeman Alijahan Patel, Rachna Y. Patil },
title = { Diagnosis of Diabetic Retinopathy using Blob Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2021 },
volume = { 183 },
number = { 32 },
month = { Oct },
year = { 2021 },
issn = { 0975-8887 },
pages = { 8-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number32/32136-2021921707/ },
doi = { 10.5120/ijca2021921707 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:18:31.961550+05:30
%A Aeman Alijahan Patel
%A Rachna Y. Patil
%T Diagnosis of Diabetic Retinopathy using Blob Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 32
%P 8-11
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Diabetic retinopathy is a not unusual place eye alignment in diabetic sufferers and is the principle reason of blindness within side the populace. Diagnosis of diabetic retinopathy at early stage protects sufferers from dropping their eyesight. This paper intends a laptop-based analysis primarily dependson the virtual processing of retinal photos which will assist human diagnosing diabetic retinopathy at early stage. The venture is finished on retina photos. The set of rules is primarily based totally on locating the blood vessels and extracting them. In this way, we actually see the complicated regions along with hemorrhages. If their numbers are large (e.g. more than 5), then the attention has enormous quantity hemorrhages, for the reason that hemorrhages at the fundus are more often than not as a result of glucose stage, then we are able to say that the attention has Diabetic Retinopathy. The most important purpose of this venture is to categories the diabetic retinopathy at any eye picture . For that, first isolates blood vessels, micro aneurysm, blot hemorrhages, fovea and hrad exudates to extract function that expect the normal eye and diabetic retinopathy eye. It may be utilized by a K-Nearest

References
  1. B. Wu, W. Zhu, F. Shi, S. Zhu, and X. Chen,“Automatic detection of microaneurysms in retinal fundus images,” Computerized Medical Imaging and Graphics, vol. 55, pp. 106–112,2017.
  2. R. Maher, S. Kayte, and D. M. Dhopeshwarkar, “Review of automated detection for diabetes retinopathy using fundus images,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 5, no. 3, pp. 1129–1136,2015.
  3. Patil, N. and Patil, R., 2018, January. Achieving Flatness: with Video Captcha, Location Tracking, Selecting the Honeywords. In 2018 International Conference on Smart City and Emerging Technology (ICSCET) (pp. 1-6). IEEE.
  4. D. J. Browning, Diabetic retinopathy: evidence-based management. Springer Science & Business Media,2010.
  5. R. Maher, S. Kayte, D. Panchal, P. Sathe, and S. Meldhe, “A decision support system for automatic screening of non- proliferative diabetic retinopathy,” International Journal of Emerging Research in Management and Technology, vol. 4, no. 10, pp. 18–24,2015.
  6. Patil, R.Y. and Devane, S.R., 2020. Hash Tree-Based Device Fingerprinting Technique for Network Forensic Investigation. In Advances in Electrical and Computer Technologies (pp. 201-209). Springer, Singapore.
  7. R. S. Maher, S. N. Kayte, S. T. Meldhe, and M. Dhopeshwarkar, “Automated diagnosis non-proliferative diabetic retinopathy in fundus images using support vector machine,” International Journal of Computer Applications, vol. 125, no. 15, pp. 7–10,2015.
  8. Gaikwad, S.R., Patil, R.Y. and Borse, D.G., 2019, January. Advanced Security in 2LQR Code Generation and Document Authentication. In 2019 International Conference on Nascent Technologies in Engineering (ICNTE) (pp. 1-4). IEEE.
  9. B. Singh and K. Jayasree, “Implementation of diabetic retinopathy detection system for enhance digital fundus images,” International Journal of advanced technology and innovation research, vol. 7, no. 6, pp. 874–876,2015.
  10. N. Thomas and T. Mahesh, “Detecting clinical features of diabetic retinopathy using image processing,” InternationalJournalofEngineeringResearch&Technology (IJERT), vol. 3, no. 8, pp. 558–561,2014.
  11. Patil, R.Y. and Ranjanikar, M., 2021, May. Biometric Authentication Based Smart Bank Locker Security System. In International Conference on Image Processing and Capsule Networks (pp. 298-308). Springer, Cham.
  12. M. Gandhi and R. Dhanasekaran, “Diagnosis of diabetic retinopathy using morphological process and SVM classifier,” in Communications and Signal Processing (ICCSP), 2013 International Conference on. IEEE, 2013, pp. 873–877
Index Terms

Computer Science
Information Sciences

Keywords

Digital Image Processing Diabetic retinopathy KNN Algorithm Machine Learning