International Journal Of Engineering Technology And Management Sciences

AUTHOR(S)

Anuja S B, Dr. F. Ramesh Dhanaseelaan

DOI: https://doi.org/10.46647/ijetms.2023.v07i01.056

ABSTRACT
Diabetic retinopathy is a complication of diabetes, caused by high blood sugar levels damaging the back of the eye (retina). It can cause blindness if left undiagnosed and untreated. However, it usually takes several years for diabetic retinopathy to reach a stage where it could threaten your sight. Diabetic retinopathy is caused by damage to the blood vessels in the tissue at the back of the eye (retina). Poorly controlled blood sugar is a risk factor. Early symptoms include floaters, blurriness, dark areas of vision and difficulty perceiving colors. Blindness can occur. Mild cases may be treated with careful diabetes management. Advanced cases may require laser treatment or surgery. DR is characterized by lesions on the retina and this paper focuses on detecting two of these lesions, Microaneurysms and Haemorrhages, which are also known as red lesions. Microaneurysms are usually the earliest visible manifestation of diabetic retinopathy. They appear as tiny red dots scattered in the retina posteriorly. They may be surrounded by a ring of yellow lipid, or hard, exudates or diabetic retinopathy that is threatening or affecting your sight, the main treatments are: Laser Treatment – to treat the growth of new blood vessels at the back of the eye (retina) in cases of proliferative diabetic retinopathy.

Page No: 382 - 389

References:

1. T. A. Ciulla, A. G. Amador, and B. Zinman, ‘‘Diabetic retinopathy and diabetic macular edema: Pathophysiology, screening, and novel therapies,’’ Diabetes Care, vol. 26, no. 9, pp. 2653–2664, Sep. 2003. 
2. R. Ganjee, R. Azmi, and M. E. Moghadam, ‘‘A novel microaneurysms detection method based on local applying of Markov random field,’’ J. Med. Syst., vol. 40, no. 3, p. 74, Mar. 2016. 
3. B. Wu, W. Zhu, F. Shi, S. Zhu, and X. Chen, ‘‘Automatic detection of microaneurysms in retinal fundus images,’’ Computerized Med. Imag. Graph., vol. 55, pp. 106–112, Jan. 2017. 
4. N. Salamat, M. M. S. Missen, and A. Rashid, ‘‘Diabetic retinopathy techniques in retinal images: A review,’’ Artif. Intell. Med., vol. 97, pp. 168–188, Jun. 2019. 
5. C. Pereira, D. Veiga, J. Mahdjoub, Z. Guessoum, L. Gonçalves, M. Ferreira, and J. Monteiro, ‘‘Using a multi-agent system approach for microaneurysm detection in fundus images,’’ Artif. Intell. Med., vol. 60, no. 3, pp. 179–188, Mar. 2014. 
6. W. Cao, N. Czarnek, J. Shan, and L. Li, ‘‘Microaneurysm detection using principal component analysis and machine learning methods,’’ IEEE Trans. Nanobiosci., vol. 17, no. 3, pp. 191–198, Jul. 2018. 
7. C. E. Baudoin, B. J. Lay, and J. C. Klein, ‘‘Automatic detection of microa neurysms in diabetic fluorescein angiography,’’ Rev. Epidemiol. Sante Publique, vol. 32, nos. 3–4, pp. 254–261, 1984. 
8. J. Denniss, I. Schiessl, V. Nourrit, C. H. Fenerty, R. Gautam, and D. B. Henson, “Relationships between visual field sensitivity and spectral ab sorption properties of the neuroretinal rim in glaucoma by multispectral imaging,” Investigative ophthalmology & visual science, vol. 52, no. 12, pp. 8732–8738, 2011. 
9. B. Khoobehi, J. M. Beach, and H. Kawano, “Hyperspectral imaging for measurement of oxygen saturation in the optic nerve head,” Investigative ophthalmology & visual science, vol. 45, no. 5, pp. 1464–1472, 2004. 
10. H. Li, W. Liu, B. Dong, J. V. Kaluzny, A. A. Fawzi, and H. F. Zhang, “Snapshot hyperspectral retinal imaging using compact spectral resolving detector array,” Journal of biophotonics, vol. 10, no. 6-7, pp. 830–839, 2017. 
11. D. S. Fong, L. Aiello, T. W. Gardner, G. L. King, G. Blankenship, J. D. Cavallerano, F. L. Ferris, and R. Klein, “Retinopathy in diabetes,” Diabetes care, vol. 27, no. suppl 1, pp. s84–s87, 2004. 
12. M. Raghu, B. Poole, J. Kleinberg, S. Ganguli, and J. S.         Dickstein, ‘‘On the expressive power of deep neural networks,’’ in Proc. 34th Int. Conf. Mach. Learn. (JMLR), vol. 70, 2017, pp. 2847–2854.
13. H. Lin and S. Jegelka, ‘‘Resnet with one-neuron hidden   layers is a universal approximator,’’ in Proc. Adv. Neural Inf. Process. Syst., 2018, pp. 6169–6178. 
14. O. Sharir and A. Shashua, ‘‘On the expressive power of overlapping archi tectures of deep learning,’’ 2017, arXiv:1703.02065. [Online]. Available: http://arxiv.org/abs/1703.02065 
15. Z. Lu, H. Pu, F. Wang, Z. Hu, and L. Wang, ‘‘The expressive power of neural networks: A view from the width,’’ in Proc. Adv. Neural Inf. Process. Syst., 2017, pp. 6231–6239. 


How to Cite This Article:
Anuja S B, Dr. F. Ramesh Dhanaseelaan . Early Detection of Diabetic Retinopathy Using Various Techniques: A Review . ijetms;7(1):361-381. DOI: 10.46647/ijetms.2023.v07i01.056