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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)

Abstract

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.

Keywords

Deep learning Glaucoma Convolutional neural networks Transfer learning 

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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

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