Lab view model for effective detection of diabetic retinopathy

https://doi.org/10.53730/ijhs.v6nS1.7299

Authors

  • M. Suganthy Associate Professor, Electronics and Communication Engineering, Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College
  • Hemalatha. S U.G Students, Electronics and Communication Engineering, Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College
  • Vinotha. M U.G Students, Electronics and Communication Engineering, Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College
  • Kirthiga. K U.G Students, Electronics and Communication Engineering, Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College

Keywords:

Diabetic Retinopathy, LABVIEW, Blood vessel, Gray level Co occurrence Matrix, Exudates, Blood clot

Abstract

Diabetic Retinopathy is a continuous damage of the retinal blood vessels caused by chronic “HYPERGLYCEMIA”. This can be caused by the problem of diabetes and it is of two types i.e Type 1, Type 2. At first, DR has no symptoms, if it is not treated it make lead to low vision and blindness. Diabetic retinopathy is one of the major reason for the blindness. As a result, adequate screening is required to detect diabetic retinopathy at an early stage. The study describes a Diabetic Retinopathy Screening System that ophthalmologists utilize as a major identifying tool to find Diabetic Retinopathy symptoms. The system uses functional forms like blood vessels, exudates, and microaneurysms in retinal pictures. By removing characteristics from divided images and using GLCM, the retinal images are divided and classed as normal or DR impacted images (Gray Level Co occurrence Matrix).The system is carried out and tested in “LAB VIEW” to justify and take measurements with less effort than traditional programming. The Lab view is the best choice for the beginners who do not have much or any experience with code or programming knowledge. 

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References

Suganthy, MAnd Manjula, S, “Performance Analysis Of Iris Biometric System Using GKPCA And SVM’’, Int. J. Information Technology and Management, vol . 20, pp.207 – 216, Inder science publishers , 2021.

DRIVE Database Retrieved From Http://Www.Isi.Uu.Nl/Research/Database/DRIVE, Accessed On 02-01-2014.

Estafanous MFG, Kaiser PK and Bowen A, “ Posterior Sub-Tenons Kenalog Injections In Diabetic Macular Edema”, Saudi Journal of Opthalmology, vol.29, pp- 270-273 , 2015.

DinialUtami, Nurul Qomariah, HandayaniTjandrasa and ChastineFatichah “Classification OfDiabetic Retinopathy And Normal Retinal Images Using CNN And SVM ”,12th International Conference on Information & Communication Technology and System (lCTS), 2019.

Neeraj Sharma and Lalit M.Aggarwal, “Automated Medical Image Segmentation Techniques” Journal Of Medical Physics, 2010, vol. 35, issue1, pp. 3–14, 2010.

Giuliari, Gian Sadaka, Ama Hinkle,David Simpson andErnest, “Current Treatments For Radiation Retinopathy”, Acta oncologica (Stockholm, Sweden),vol .50, pp.6-13 , 2011.

XianglongZeng,HaiquanChen,Yuan Luo and Wenbin Ye, “Automated Diabetic Retinopathy Detection Based on Binocular Siamese-Like Convolutional Neural Network”,IEEE Access pp(99):1-1,2019.

V.V.K. Raju, S.Sagar, B.Divya, K.Madhulika andD.Sandeep,“Retinal Image Analysis For Diabetic Retinopathy”, The international journal of analytical and experimental modal analysis ,vol. 12, Issue 5 ,2020.

Kumar. S and Kumar.B,“Diabetic Retinopathy Detection By Extracting Area And Number Of Microaneurysm From Color Fundus Image”, 5th International Conference on Signal Processing and IntegratedNetworks (SPIN),pp. 359-364, IEEE,2018.

Betteena Sheryl Fernando, Heena Firdaus, Alexander and Jothimanikandan R, “Diabetic Retinopathy Detection Using Retinal Images”, Journal of advances in Engineering and Management (IJAEM), vol.3, issue 3, pp.230-237, 2021.

Riccardo Cheloni,Stefano A Gandolfi,Carlo Signorelli and Anna odone , “Global Prevalence Of Diabetic Retinopathy Protocol For A Systematic Review And Meta Analysis, published on BMJ, vol.9, issue 3, 2019.

Vijaya Kumari.V andSuriyaNarayanan. N,“Diabetic Retinopathy-Early Detection Using Image Processing Techniques”, International Journal on Computer Science and Engineering,vol.2, no.2, pp. 357-361, 2010.

Priya.R andAruna.P, “Review Of Automated Diagnosis Of Diabetic Retinopathy Using The Support Vector Machine”, International journal of applied engineering research, vol. 1, no. 4, pp. 844-863, 2011.

Suvajit Dutta, Bonthala CS Manideep, Syed Muzamil Basha, Ronnie D. Caytiles and N. Ch. S. N. Iyenga, “Classification OfDiabetic Retinopathy Images By Using Deep Learning Models,” International journal of Grid and Distributed Computing, vol .11, pp. 89-106, 2018.

Jestin V.K, Anitha J and Jude Hemanth, “Texture Feature Extraction For Retinal Image Processing”, In International Conference on Computing, Electronics and Electrical Technologies (ICCEET), pp. 548-551, 2012.

Published

13-05-2022

How to Cite

Suganthy, M., Hemalatha, S., Vinotha, M., & Kirthiga, K. (2022). Lab view model for effective detection of diabetic retinopathy. International Journal of Health Sciences, 6(S1), 10584–10595. https://doi.org/10.53730/ijhs.v6nS1.7299

Issue

Section

Peer Review Articles