Abstract
Diabetic retinopathy is a chronic progressive eye disease associated to a group of eye problems as a complication of diabetes. This disease may cause severe vision loss or even blindness. Specialists analyze fundus images in order to diagnostic it and to give specific treatments. Fundus images are photographs taken of the retina using a retinal camera, this is a noninvasive medical procedure that provides a way to analyze the retina in patients with diabetes. The correct classification of these images depends on the ability and experience of specialists, and also the quality of the images. In this paper we present a method for diabetic retinopathy detection. This method is divided into two stages: in the first one, we have used local binary patterns (LBP) to extract local features, while in the second stage, we have applied artificial neural networks, random forest and support vector machines for the detection task. Preliminary results show that random forest was the best classifier with 97.46% of accuracy, using a data set of 71 images.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Bonafarte, S., et al.: Retinopatía diabética. Elsevier (1997)
Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)
Burges, C.: A tutorial on support vector machines for pattern recognition. Proceedings of Data Mining and Knowledge Discovery 2, 121–167 (1998)
Ege, B.M., Hejlesen, O.K., Larsen, O.V., Moller, K., Jennings, B., Kerr, D., Cavan, D.A.: Screening for diabetic retinopathy using computer based image analysis and statistical classification. Computer Methods and Programs in Biomedicine 62(3), 165–175 (2000)
Gowda, A., et al.: Exudates detection in retinal images using back propagation neural networks. International Journal of Computer Applications 25 (2011)
Hem, K., et al.: Fluorescein angiosgraphy: A user’s manual (2008)
International Diabetes Federation. IDF Diabetes Atlas153, 7–68 (2013)
Kavitha, D., Duraiswamy, K.: Automatic detection of hard and soft exudates in fundus images using color histogram thresholding. European Journal of Science Research 48, 493–504 (2011)
Mitchell, T.: Machine learning. McGrawHill (1997)
Niemeijer, M., et al.: Automatic detection and differentiation of drusen, exudates and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. Investigative Opthalmology and Visual Science 48 (2007)
Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distribution. Pattern Recognition 29(1), 51–59 (1996)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Osareh, A., et al.: Automatic recognition of exudative maculopathy using fuzzy c-means clustering and neural networks. Medical Image Understanding and Analysis 3, 49–52 (2001)
Silberman, N., et al. Case for automated detection of diabetic retinopathy. In AAAI Spring Symposium on AI for Development, (2010)
World Health Organization. Diabetes mellitus. Media Centre (2014)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
de la Calleja, J., Tecuapetla, L., Auxilio Medina, M., Bárcenas, E., Urbina Nájera, A.B. (2014). LBP and Machine Learning for Diabetic Retinopathy Detection. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2014. IDEAL 2014. Lecture Notes in Computer Science, vol 8669. Springer, Cham. https://doi.org/10.1007/978-3-319-10840-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-10840-7_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10839-1
Online ISBN: 978-3-319-10840-7
eBook Packages: Computer ScienceComputer Science (R0)