Vol. 1 No. 6 (2019): Volume 1, Issue 6, year 2019

Sustainable Diabetic Retinopathy Diagnosis System Using Iot

Balaramesh C
UG Student, ECE Department, DMI Engineering College, Aralvoimozhi, Kanyakumari District, Tamil Nadu, India.
Jerin L
UG Student, ECE Department, DMI Engineering College, Aralvoimozhi, Kanyakumari District, Tamil Nadu, India
Hari Krishna K
UG Student, ECE Department, DMI Engineering College, Aralvoimozhi, Kanyakumari District, Tamil Nadu, India
Shanmugam G
UG Student, ECE Department, DMI Engineering College, Aralvoimozhi, Kanyakumari District, Tamil Nadu, India
Asbin Thomas Wizar L
UG Student, ECE Department, DMI Engineering College, Aralvoimozhi, Kanyakumari District, Tamil Nadu, India.
Published November 2, 2019
  • Sustainable Computing,
  • Internet of Things,
  • Image Segmentation,
  • Diabetic Retinopathy,
  • Smart Devices
How to Cite
C, B., L, J., K, H. K., G, S., & L, A. T. W. (2019). Sustainable Diabetic Retinopathy Diagnosis System Using Iot. International Research Journal of Multidisciplinary Technovation, 1(6), 71-80. Retrieved from https://mapletreejournals.com/index.php/irjmt/article/view/264


Supportable processing gives a remote access to the finding framework for simple and quick usage. The proposed approach estimates glucose level in the blood through Dexcom G4 Platinum sensors on diabetic patients. In light of the readings, Internet of Things (IoT) stage offer a reasonable answer for Diabetic Retinopathy. The motivation behind this research is to spare the life of the patient from vision misfortune. The procedure begins from the gadgets themselves which safely move data with IoT stage and vow the regular language for the portable applications to work together with one another. This stage always accumulates a huge number of data from the gadget and store in a protected database. It fuses the information got from IoT gadgets and applies investigation to anticipate significant information to address clinical needs. The outcomes shown by the execution of the proposed methodologies are practically identical with the modern frameworks in relations of exactness, particularity and affectability. The proposed method performs superior to different systems by accomplishing a normal 99.58% Precision, 72.51% Sensitivity and 99.83% Specificity in the trial arrangement.


Download data is not yet available.


1. Toni Adame, Albert Bel, Anna Carreras, Joan Melia-Segui, MiquelOliver,RafaelPous, CUIDATS: An RFID–WSN hybrid monitoring system for smart health care environments, Future Generation Computer Systems, Vol 78,pp. 602– 615, 2018.
2. George Azzopardi, Nicola Strisciuglio, Mario Vento, Nicolai Petkov, Trainable COSFIRE filters for vessel delineation with application to retinal images, Medical Image Analysis Vol 19,pp.46–57, 2015.
3. LiyeGuo, Ji-Jiang Yang, Lihui Peng, Jianqiang Li, Qingfeng Liang, A computer-aided healthcare system for cataract classification and grading based on fundus image analysis, Computers in Industry Vol 69, pp. 72–80, 2015.
4. SohiniRoychowdhury, Dara D. Koozekanani, Keshab K. Parhi, Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classification, IEEE Journal of Biomedical and Health Informatics, Vol. 19, No. 3, May2015.
5. ArgyriosChristodoulidis, Thomas Hurtut, Houssem Ben Tahar, Farida Cheriet, A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images, Computerized Medical Imaging and Graphics, Vol 52, pp 28-43, September 2016.
6. R.GeethaRamani, Lakshmi Balasubramanian, Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis, Biocybernetics and Biomedical Engineering, Vol 36, Issue 1, pp 102-118, 2016.
7. Shahab Aslani, HaldunSarnel, A new supervised retinal vessel segmentation method based on robust hybrid features, Biomedical Signal Processing and Control, Vol30, pp 1-12, September 2016.
8. Anushikha Singh, Malay Kishore Dutta, Dilip Kumar Sharma, Unique identification code for medical fundus images using blood vessel pattern, Computer Methods and Programs in Biomedicine, Vol 135, pp 61-75, October 2016.
9. MoeenHassanalieragh, Alex Page, TolgaSoyata, Gaurav Sharma, Mehmet Aktas, Gonzalo Mateos,BurakKantarci, SilvanaAndreescu, Health Monitoring andManagement Using Internet-of-Things (IoT) Sensing with Cloud-based Processing: Opportunities and Challenges, 2015 IEEE International Conference on ServicesComputing.
10. Richard K. Lomotey, Joseph Pry, SumanthSriramoju,Wearable IoT data stream traceability in a distributed health information system, Pervasive and Mobile Computing, Vol 40,pp 692–707, 2017.
11. SanazRahimiMoosavi, Tuan Nguyen Gia, Ethiopia Nigussie, Amir M. Rahmani,Seppo Virtanen, HannuTenhunen, JouniIsoaho, End-to-end security scheme for mobility enabled healthcare Internet of Things, Future Generation Computer Systems, Vol 64,pp 108–124, 2016.
12. Martin Henze, Lars Hermerschmidt, Daniel Kerpen, Roger Haubling, Bernhard Rumpe, Klaus Wehrle,A comprehensive approach to privacy in the cloud-based Internet ofThings, Future Generation Computer Systems, Vol 56,pp 701–718, 2016.
13. JayavardhanaGubbi, RajkumarBuyya, SlavenMarusi, MarimuthuPalaniswami, Internet of Things (IoT): A vision, architectural elements, and future directions, Future Generation Computer Systems, Vol 29, pp 1645–1660, 2013.
14. S. M. Riazul Islam, DaehanKwak, Md. HumaunKabir, Mahmud Hossain, And Kyung-Sup Kwak,The Internet of Things for Health Care: A Comprehensive Survey, IEEE Access The journal for rapid open access publishing, June 1, 2015.
15. Diabetic Atlas available
16. Shuangling Wang, YilongYin, GuibaoCao, BenzhengWei, YuanjieZhengGongping Yang, Hierarchical retinal blood vessel segmentation based on feature and ensemble learning,eurocomputing, Vol 149, pp 708–717, 2015.
17. E.Rajaby, S.M.Ahadi, H.Aghaeinia, Robust color image segmentation using fuzzy c-means with weighted hue and intensity, Digital Signal Processing, Vol 51, pp.170–183, 2016.
18. Adrian Stetco, Xiao-Jun Zeng, John Keane, Fuzzy C-means++: Fuzzy C-means with effective seeding initialization, Expert Systems with Applications, Vol 42, pp. 7541–7548, 2015.
19. Buket D. Barkana, InciSaricicek ,BurakYildirim , Performance analysis of descriptive statistical features in retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier fusion, Knowledge-Based Systems, Vol 118, pp. 165– 176, 2017.
20. K.S.Sreejini, V.K.Govindan, Improved multiscale matched filter for retina vessel segmentation using PSO algorithm, Egyptian Informatics Journal,Vol 16,pp.253–260, 2015.
21. Sidra Rashid and Shagufta, Computerized Exudate Detection in Fundus Images Using Statistical Feature based Fuzzy C-mean Clustering, Int. J. Com. Dig. Sys. Vol 2, pp. 135-145, 2013.
22. SudeshnaSilKar, Santi P. Maity, Retinal blood vessel extraction using tunable bandpass filter and fuzzy conditional entropy, computer methods and programs in biomedicine, Vol 133, pp111-132, 2016.
23. NagendraPratap Singh, Rajeev Srivastava, Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter, Computermethods and programs in biomedicine, Vol 129, pp. 40-50, 2016.