Detection and classification of Non-Proliferative Diabetic Retinopathy using a Back-Propagation neural network

Authors

  • Alberto Jorge Rosales Silva Instituto Politécnico Nacional https://orcid.org/0000-0001-8436-3025
  • Jesús Salvador Velázquez-González Instituto Politécnico Nacional
  • Francisco Javier Gallegos-Funes Instituto Politécnico Nacional https://orcid.org/0000-0002-4854-6438
  • Guadalupe de Jesús Guzmán-Bárcenas Instituto Politécnico Nacional

Keywords:

Diabetic Retinopathy, early diagnosis, automatically identification, fundus images

Abstract


One of the most serious complications of type 2 Diabetes Mellitus (DM) is the Diabetic Retinopathy (DR). DR is a silent disease and is only recognized when the changes on the retina have progressed to a level at which treatment turns complicate, so an early diagnosis and referral to an ophthalmologist or optometrist for the management of this disease can prevent 98% of severe visual loss. The aim of this work is to automatically identify Non Diabetic Retinopathy (NDR), and Background Retinopathy using fundus images. Our results show a classification accuracy of 92%, with sensitivity and specifity of 95%.

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Author Biographies

Alberto Jorge Rosales Silva, Instituto Politécnico Nacional

Escuela Superior de Ingeniería Mecánica y Eléctrica Unidad Zacatenco

Jesús Salvador Velázquez-González, Instituto Politécnico Nacional

Escuela Superior de Ingeniería Mecánica y Eléctrica Unidad Zacatenco

Francisco Javier Gallegos-Funes, Instituto Politécnico Nacional

Escuela Superior de Ingeniería Mecánica y Eléctrica Unidad Zacatenco

Guadalupe de Jesús Guzmán-Bárcenas, Instituto Politécnico Nacional

Centro Interdisciplinario de Ciencias de la Salud Unidad Santo Tomas

References

Organización Mundial de la Salud. Diabetes. Nota descriptiva N.° 312. Available on: http://www.who.int/mediacentre/factsheets/fs312/es/index.html Accessed: October 9, 2014.

R. Frank. “Diabetic Retinopathy”. The New England Journal of Medicine. Vol. 350. 2004. pp. 48-58.

D. Fong, L. Aiello, T. Gardner, G. King, G. Blankenship, et al. “Retinopathy in Diabetes”. Diabetes Care. Vol. 27. 2004. pp. 584-587.

D. Browning. Diabetic Retinopathy: Evidence Based Management. 1st ed. Ed. Springer. New York, USA. 2010. pp. 31-61.

L. Verma, G. Prakash, H. Tewari. “Diabetic Retinopathy: Time for Action. No complacency please”. Articles from Bulletin of the World Health Organization. Vol. 80. 2002. pp. 419-420.

S. Le, E. Lee, R. Kingsley, Y. Wan, D. Russell, R. Klein, A. Warn. “Comparison of Diagnosis of Early Retinal Lesions of Diabetic Retinopathy Between a Computer System and Human Experts”. Arch. Ophthalmol. Vol. 119. 2001. pp. 509-515.

M. El-Bab, N. Shawky, A. Al-Sisi, M. Akhtar. “Retinopathy and risk factors in diabetic patients from Al-Madinah Al-Munawarah in the Kingdom of Saudi Arabia”. Clinical Ophthalmology. Vol. 6. 2012. pp. 269-276.

A. Khurana. Comprehensive Ophthalmology. 4th ed. Ed. New Age International (P) Ltd., Publishers. New Delhi, India. 2007.

A. Fleming, S. Philip, K. Goatman, J. Olson, P. Sharp. “Automated microaneurysm detection using local contrast normalization and local vessel detection”. IEEE Transactions in Medical Imaging. Vol. 25. 2006. pp. 1223-1232.

T. Walter, J. Klein, P. Massin, A. Erginay. “A contribution of image processing to the diagnosis of diabetic retinopathy - detection of exudates in color fundus images of the human retina”. IEEE Transactions on Medical Imaging. Vol. 21. 2002. pp. 1236-1243.

M. Giger, N. Karssemeijer, S. Armato. “ComputerAided Diagnosis in Medical Imaging”. IEEE Trans. Med. Imag. Vol. 20. 2001. pp. 1205-1208.

G. Gardner, D. Keating, T. Williamson, A. Elliot. “Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool”. British Journal of Ophthalmology. Vol. 80. 1996. pp. 940-944.

E. Chaum, T. Karnowski, V. Govindasamy, M. Abdelrahman, K. Tobin. “Automated Diagnosis of Retinopathy by content-based image retrieval”. The Journal of Retinal and Vitreous Diseases. Vol. 28. 2008. pp. 1463-1477.

J. Anitha, D. Selvathi, D. Hemanth. Neural Computing Based Abnormality Detection in Retinal Optical Images. Proceedings of the IEEE International Advance Computing Conference (IACC). Patiala, India, 2009. pp. 630-635.

N. Singh, R. Chandra. “Automated Early Detection of Diabetic Retinopathy Using Image Analysis Techniques”. International Journal of Computer Applications. Vol. 8. 2010. pp. 18- 23.

D. Selvathi, N. Prakash, N. Balagopal. “Automated Detection of Diabetic Retinopathy for Early Diagnosis using Feature Extraction and Support Vector Machine”. International Journal of Emerging Technology and Advanced Engineering. Vol. 2. 2013. pp. 103-108.

R. Radha, B. Lakshman. “Retinal Image Analysis Using Morphological Process and Clustering Technique”. Signal and Image Processing: An International Journal (SIPIJ). Vol. 4. 2013. pp. 55-69.

C. Stellingwerf, P. Hardus, J. Hooymans. “Assessing Diabetic Retinopathy using two-field digital photography and the influence of JPEG-compression”. Documenta Ophthalmologia. Vol. 108. 2004. pp. 203- 209.

MESSIDOR, TECHNO-VISION. MESSIDOR: methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology. 2014. Available on: http://messidor.crihan.fr/index-en.php Accessed: October 9, 2014.

R. Gonzalez, R. Woods, S. Eddins. Digital Image Processing using MATLAB. 1st ed. Ed. Gatesmark Publishing. Knoxville, USA. 2009. pp. 334-358.

P. Qiu. Image Processing and Jump Regression Analysis. 1st ed. Ed. John Wiley & Sons, Inc. New Jersey, USA. 2005. pp. 2-205.

J. Nayak, P. Bhat, R. Acharya, C. Lim, M. Kagathi. “Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Images”. J. Med. Syst. Vol. 32. 2008. pp. 107-115.

N. Otsu. “A Threshold Selection Method from GrayLevel Histograms”. IEEE Transactions on Systems, Man and Cybernetics. Vol. 9. 1979. pp. 62-66.

F. Cui, L. Zou, B. Song. Edge Feature Extraction Based on Digital Image Processing Techniques. Proceedings of the IEEE Int. Conference on Automation and Logistics. Qingdao, China. 2008. pp. 2320-2324.

J. Canny. “A Computational Approach to Edge Detection”. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 8. 1986. pp. 679-698.

L. Boroczky, P. Cremonesi, N. Scarabottolo. Texture Analysis for Image Processing on General-Purpose Parallel Machines. Proceedings of the International Symposium on Parallel Architectures, Algorithms and Networks. Budapest, Hungary. 1994. pp. 17-24.

Centre for Innovation in Mathematics Teaching (CIMT). Analysis of variance (Anova). Available on: http://www.cimt.plymouth.ac.uk/projects/mepres/alevel/fstats_ch7.pdf, Accessed: November 9, 2014.

A. Akobeng. “Understanding diagnostic test 1: sensitivity, specificity and predictive values”. Foundation Acta Pædiatrica/Acta Pædiatrica. Vol. 96. 2006. pp. 338-341.

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Published

2015-02-18

How to Cite

Rosales Silva, A. J., Velázquez-González, J. S., Gallegos-Funes, F. J., & Guzmán-Bárcenas, G. de J. (2015). Detection and classification of Non-Proliferative Diabetic Retinopathy using a Back-Propagation neural network. Revista Facultad De Ingeniería Universidad De Antioquia, (74), 70–85. Retrieved from https://revistas.udea.edu.co/index.php/ingenieria/article/view/18472