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Classification of Retina Diseases from OCT using Genetic Programming

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Hadeel Abdulrahman, Mohamed Khatib

Hadeel Abdulrahman and Mohamed Khatib. Classification of Retina Diseases from OCT using Genetic Programming. International Journal of Computer Applications 177(45):41-46, March 2020. BibTeX

	author = {Hadeel Abdulrahman and Mohamed Khatib},
	title = {Classification of Retina Diseases from OCT using Genetic Programming},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2020},
	volume = {177},
	number = {45},
	month = {Mar},
	year = {2020},
	issn = {0975-8887},
	pages = {41-46},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2020919973},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In this paper, a fully automated method for feature extraction and classification of retina diseases is implemented. The main idea is to find a method that can extract the important features from the Optical Coherence Tomography (OCT) image, and acquire a higher classification accuracy. The using of genetic programming (GP) can achieve that aim. Genetic programming is a good way to choose the best combination of feature extraction methods from a set of feature extraction methods and determine the proper parameters for each one of the selected extraction methods. 800 OCT images are used in the proposed method, of the most three popular retinal diseases: Choroidal neovascularization (CNV), Diabetic Macular Edema (DME) and Drusen, beside the normal OCT images. While the set of the feature extraction methods that is used in this paper contains: Gabor filter, Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), histogram of the image, and Speed Up Robust Filter (SURF). These methods are used for the both of global and local feature extraction. After that the classification process is achieved by the Support Vector Machine (SVM). The proposed method performed high accuracy as compared with the traditional methods.


  1. Adhi, M. and Duker, J. S.,2013. Optical coherence tomography – current and future applications. Wolters Kluwer Health, Lippincott Williams & Wilkins.
  2. Koza, J. R., 1992. Genetic Programming on the Programming of Computers by Means of Natural Selection. The MIT Press Cambridge, Massachusetts London, England.
  3. Bi, Y., Zhang, M., and Bing, X. 2018. Genetic Programming for Automatic Global and Local Feature Extraction to Image Classification. IEEE Congress on Evolutionary Computation.
  4. Bi, Y., Zhang, M., and Bing, X. 2018. An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming. Springer International Publishing AG, part of Springer Nature.
  5. Dash, P., Sigappi, AN. 2018. Detection and Recognition of Diabetic Macular Edema from OCT Images Based on Local Feature Descriptor. International Journal of Pure and Applied Mathematics.
  6. Lensen, A., Al-Sahaf, H., Zhang, M., and Xue, B. 2016. Genetic Programming for Region Detection, Feature Extraction, Feature Construction and Classification in Image Data. Springer International Publishing Switzerland.
  7. Ul Ain, Q. Xue,B., Al-Sahaf, H., and Zhang,M. 2018. Genetic Programming for Feature Selection and Feature Construction in Skin Cancer Image Classification. Springer Nature Switzerland AG.
  8. Gholami, P., Hassani,M. S., Parthasarathy, M. K., Zelek, J., and Lakshminarayanan, V. 2018. Classification of Optical Coherence Tomography images for diagnosing different ocular diseases. Multimodal Biomedical Imaging XIII
  9. Montana, D. 2002. Strongly Typed Genetic Programming.
  10. Gonzalez, R., and Woods, R., Digital Image Processing, third edition, Prentice Hall, 2009.
  11. Feichtinger, H., and Strohmer, T., 1998, Gabor Analysis and Algorithms: Theory and Applications, Birkhäuser.
  12. Wang, L., and HE, DC., 1990, Texture Classification Using Texture Spectrum.
  13. Bay, H., Tuytelaars, T., and Gool, L. V., 2008, SURF: Speeded Up Robust Features.
  14. Haralick R.M., Shanmugan, K., and Dinstein, I., 1979. Statistical and Structural Approaches to Texture. Proc.IEEE 67:786-804.
  15. Kermany, D., Zhang, K., and Goldbaum, M. 2018, Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification. Mendeley Data, v2


Genetic programming, feature extraction, Optical Coherence Tomography, OCT image classification, OCT feature extraction.