Performance Comparison of Pre-trained Deep Neural Networks for Automated Glaucoma Detection

  • Manas Sushil
  • G. Suguna
  • R. LavanyaEmail author
  • M. Nirmala Devi
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


This paper addresses automated glaucoma detection system using pre-trained convolutional neural networks (CNNs). CNNs, a class of deep neural networks (DNNs), extract features of high-level abstractions from the fundus images, thereby eliminating the need for hand-crafted features which are prone to inaccuracies in segmenting landmark regions and require excessive involvement of experts for annotating these landmarks. This work investigates the applicability of pre-trained CNNs for glaucoma diagnosis, which is preferred when the dataset size is small. Further, pre-trained networks have the advantage of the quick model building. The proposed system has been validated on the High-Resolution (HRF), which is a publicly available benchmark database. Results demonstrate that among other pre-trained CNNs, VGG16 network is more suitable for glaucoma diagnosis.


Deep learning Glaucoma Convolutional neural networks Transfer learning 


  1. 1.
    Dharani V, Lavanya R (2017) Improved microaneurysm detection in fundus images for diagnosis of diabetic retinopathy. In: International symposium on signal processing and intelligent recognition systems. Springer, pp 185–198 (2017)Google Scholar
  2. 2.
    Sharma A, Subramaniam SD, Ramachandran KI, Lakshmikanthan C, Krishna S, Sundaramoorthy SK (2016) Smartphone-based fundus camera device (MII Ret Cam) and technique with ability to image peripheral retina. Eur J Ophthalmol 26(2):142–144CrossRefGoogle Scholar
  3. 3.
    Richter GM, Anne LC (2016) Minimally invasive glaucoma surgery: current status and future prospects. Clin Ophthalmol (Auckland, NZ) 10:189–206Google Scholar
  4. 4.
    Zhang Z, Srivastava R, Liu H, Chen X, Duan D, Wong WK, Kwoh CK, Wong TY, Liu Y (2014) A survey on computer aided diagnosis for ocular diseases. BMC Med Inf Decis Making 14(1):80CrossRefGoogle Scholar
  5. 5.
    Haleem MS, Han L, van Hemert J, Li B (2013) Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review. Comput Med Imaging Graph 37(7):581–596CrossRefGoogle Scholar
  6. 6.
    Nayak J, Acharya R, Bhat PS, Shetty N, Lim TC (2009) Automated diagnosis of glaucoma using digital fundus images. J Med Syst 33(5):337CrossRefGoogle Scholar
  7. 7.
    Bock R, Meier J, Nyúl LG, Hornegger J, Michelson G (2010) Glaucoma risk index: automated glaucoma detection from color fundus images. Med Image Anal 14(3):471–481CrossRefGoogle Scholar
  8. 8.
    Dua S, Acharya UR, Chowriappa P, Sree SV (2010) Wavelet-based energy features for glaucomatous image classification. IEEE Trans Inf Technol Biomed 16(1):80–87CrossRefGoogle Scholar
  9. 9.
    Chen X, Xu Y, Wong DWK, Wong TY, Liu J (2015) Glaucoma detection based on deep convolutional neural network. IEEE EMBC 2015:715–718Google Scholar
  10. 10.
    Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Networks 61:85–117CrossRefGoogle Scholar
  11. 11.
    Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Kim R (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410CrossRefGoogle Scholar
  12. 12.
    Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
  13. 13.
    Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2015) Imagenet: a large-scale hierarchical image database. IEEE CVPR 2009:248–255Google Scholar
  14. 14.
    Odstrcilik J, Kolar R, Budai A, Hornegger J, Jan J, Gazarek J, Angelopoulou E (2013) Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database. IET Image Proc 7(4):373–383MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Manas Sushil
    • 1
  • G. Suguna
    • 1
  • R. Lavanya
    • 1
    Email author
  • M. Nirmala Devi
    • 1
  1. 1.Department of Electronics and Communication EngineeringAmrita School of EngineeringCoimbatoreIndia

Personalised recommendations