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Review on: Detection of Diabetic Retinopathy using SVM and MDA

International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 117 - Number 20
Year of Publication: 2015
Gurmeen Kaur

Shveta and Gurmeen Kaur. Article: Review on: Detection of Diabetic Retinopathy using SVM and MDA. International Journal of Computer Applications 117(20):1-3, May 2015. Full text available. BibTeX

	author = {Shveta and Gurmeen Kaur},
	title = {Article: Review on: Detection of Diabetic Retinopathy using SVM and MDA},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {117},
	number = {20},
	pages = {1-3},
	month = {May},
	note = {Full text available}


Diabetes is the commonest cause of blindness in the working age group these days. Diabetes can affect the sight of a patient and thus it results in causing glaucoma, cataracts and its most severe effect is on blood vessels inside the eye as the blood vessels are damaged and it reaches a condition known as "diabetic retinopathy" which can also be called as eye blindness due to diabetes. Automatic detection of retinal abnormalities is commonly performed for haemorrhages, micro aneurysms, cotton wool spot and hard exudates. However, if more attention is paid to it, there is worse case of retinal abnormality called neovascularisation but much research was not done to detect it. In this case, new blood vessels branch out due to extensive lack of oxygen in the retinal capillaries. So the automated analysis of human eye fundus image is an important task as it can later lead to sectional blindness or thorough blindness. If desired quota of measures is taken and methods are put into consideration then it can be potentially reduced to 50%. In this paper, we present a review for the detection of DR using fundus images and approaches SVM and MDA.


  • Sohini Roychowdhury, Student Member, IEEE, Dara D. Koozekanani, Member, IEEE and Keshab K. Parhi Fellow, IEEE (2013)," DREAM: Diabetic Retinopathy Analysis using
  • Machine Learning", IEEE.
  • Doaa Youssef, Nahed H. Solouma, NILES, Cairo University, Giza, Egypt (2012), "Accurate detection of blood vessels improves the detection of exudates in color fundus images", ELSEVIER.
  • Murugan. R, Dr. Reeba Korah, (2012)," An Automatic Screening Method To Detect Optic Disc In The Retina", International Journal of Advanced Information Technology (IJAIT) Vol. 2, No. 4.
  • LiliXu, ShuqianLuo, (2010), "A novel method for blood vessel detection from retinal images", BioMedical Engineering OnLine.
  • Mohammed AlRawi, Munib Qutaishat, Mohammed Arrar, (2006), "An improved matched filter for blood vessel detection of digital retinal images", Computers in Biology and Medicine, pp 262 – 267.
  • Herbert F. Jelinek , Michael J. Cree , Jorge J. G. , May (2007), "Automated segmentation of retinal blood vessels and identification of proliferative Leandro , João V. B. Soars and Roberto M. Cesar, Jr. A. Luckie diabetic retinopathy ", Optical society of America, 24, pp 14481456.
  • Priya. R , Aruna. P, (2011), "Review of automated diagnosis of diabetic retinopathy using the support vector machine", IJAER.
  • Edgardo FelipeRiveron1 and Noel Garcia Guimeras, (2006), "Extraction of Blood Vessels in Ophthalmic Color Images of Human Retinas", CIARP 2006, LNCS 4225, pp. 118 – 126, Springer Verlag Berlin Heidelberg.
  • Bevilacqua V. , Cambò, S. Cariello, L. Mastronardi , G . , (2005), "A combined method to detect Retinal Fundus Features", Conference on EACDA, Italy.
  • V. Vijayakumari, N. Suriyanarayanan, (2012)," Survey on the Detection Methods of Blood Vessel in Retinal Images", European Journal of Scientific ResearchISSN 1450-216X Vol. 68 No. 1 (2012), pp. 83-92© EuroJournals Publishing, Inc. 2012 http://www. europeanjournalofscientificresearch. com .
  • Shilpa Joshi, Dr P. T. Karule, (2012),"Retinal Blood Vessel Segmentation", International Journal of Engineering and Innovative Technology (IJEIT) Volume 1, Issue 3.
  • Berrichi Fatima Zohra, Benyettou Mohamed,"Automated diagnosis of retinal images using the Support Vector Machine (SVM)", 1Laboratoire de Modélisation et Optimisation des Systèmes Industriels: LAMOSI. Faculté des Sciences, Département d'Informatique, USTO. B. P. 1505 EL M'NAOUER 31000 ORAN – ALGERIE.
  • Asha Gowda Karegowda, Asfiya Nasiha, M. A. Jayaram, A. S . Manjunath, (2011), "Exudates Detection in Retinal Images using Back Propagation Neural Network", International Journal of Computer Applications (0975 – 8887) Volume 25– No. 3.