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Automatic Classification of Pterygium-Non Pterygium Images Using Deep Learning

Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB,volume 34)


Pterygium is an ocular disease caused by the invasion of a fibro-vascular tissue onto the cornea region. Several researches has been developed for automatic detection of pterygium in eyes images. In those researches, color and shape information of pterygium were explored using Digital Image Processing techniques and Machine Learning algorithms such as Artificial Neural Networks (ANN) and Support Vector Machine (SVM). More recently, Deep Learning techniques were applied for implementing a system for diagnosing multiple ocular diseases including pterygium, however no study have been developed on using Deep Learning focused on ptyregium detection only. We present a method for automatic classification of pterygium - non pterygium images using Convolutional Neural Networks (CNN). A dataset of positive (pterygium) and negative (non pterygium) images, previously used in early researches, was employed in order to train and to test a CNN model with one convolutional layer. The images were studied in two color formats, RGB and grayscale. The best result in the pterygium – non pterygium image classification task was attained using RGB format, getting an Area Under the ROC curve of 99.4%. The results obtained overcome the results found in literature.


  • Classification
  • Convolutional Neural Network
  • Deep learning
  • Pterygium

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  • DOI: 10.1007/978-3-030-32040-9_40
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    False Positive: Non-pterygium image mistakenly classified as Pterygium.

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    False Negative: Pterygium image mistakenly classified as Non-pterygium.


  1. Todorovic, D., Vulovic, T., Sreckovic, S., Jovanovic, S., Janicijevic, K., Todorovic, Z.: Updates on the treatment of pterygium. Serbian J. Exp. Clin. Res. 17(3), 257–262 (2016)

    CAS  CrossRef  Google Scholar 

  2. Hossain, P.: Pterygium Surgery, Autumn edn. Royal College of Ophthalmologists, London (2011)

    Google Scholar 

  3. Hall, A.: Understanding and managing pterygium. Community Eye Health 29(95), 54–56 (2016)

    PubMed  PubMed Central  Google Scholar 

  4. Hellem, A.: Pterygium: what is “surfer’s eye”? (2017). Accessed 10 Aug 2018

  5. Zaki, W.M.D.W., Daud, M.M., Abdani, S.R., Hussain, A., Mutalib, H.A.: Automated pterygium detection method of anterior segment photographed images. Comput. Methods Programs Biomed. J. 154, 71–78 (2017)

    CrossRef  Google Scholar 

  6. Zhang, K., et al.: Works citing “an interpretable and expandable deep learning diagnostic system for multiple ocular diseases: qualitative study”. J. Med. Internet Res. 20, 11 (2018)

    CrossRef  Google Scholar 

  7. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016).

  8. Gao, X., et al.: Automatic pterygium detection on cornea images to enhance computer-aided cortical cataract grading system. Presented at the 34th Annual International Conference of the IEEE EMBS, San Diego, California USA (2012)

    Google Scholar 

  9. Mesquita, R.G., Figueiredo, E.M.N.: An algorithm for measuring pterygium’s progress in already diagnosed eyes. Presented at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan (2012)

    Google Scholar 

  10. Buitelaar, P., Cimiano, P.: Frontiers in Artificial Intelligence and Applications, vol. 167. IOS Press, Amsterdam (2008)

    Google Scholar 

  11. Proenc, H., Alexandre, L.A.: UBIRIS: a noisy iris image database. In: International Conference on Image Analysis and Processing (2005)

    Google Scholar 

  12. Miles, J.: A selection of sample iris photos (grouped by illumination type), 18 November 2015.

  13. Lawrence, H.: The Australian pterigium institute.

  14. American Academy of Ophthalmology 2018: Advanced ophthalmology Inc. (2015)

  15. Mitchell, T.M.: Machine Learning. McGraw-Hill Inc, New York (1997)

    Google Scholar 

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We would like to thank Professor Lawrence Hirst of the Australia Pterygium Center for granting access to the use of the pterygium databases and to Professor Rafael for the Brazilian pterygium database. We also thank the other database providers cited in the article.

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Correspondence to Luis Rojas Aguilera .

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Lopez, Y.P., Aguilera, L.R. (2019). Automatic Classification of Pterygium-Non Pterygium Images Using Deep Learning. In: Tavares, J., Natal Jorge, R. (eds) VipIMAGE 2019. VipIMAGE 2019. Lecture Notes in Computational Vision and Biomechanics, vol 34. Springer, Cham.

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