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A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential to identify glaucoma early through quantitative analysis of digital fundus images. Preparing an effective model for CAD requires a large database. This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach. An autoencoder is trained to determine effective and important features from fundus images. These features are used to develop classes of glaucoma for testing. The method achieved an F − measure value of 0.95 utilizing 1426 digital fundus images (589 control and 837 glaucoma). The efficacy of the system is evident, and is suggestive of its possible utility as an additional tool for verification of clinical decisions.

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Authors and Affiliations

  1. Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India

    U. Raghavendra, Anjan Gudigar & Tejaswi N. Rao

  2. Department of Ophthalmology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, 576104, India

    Sulatha V. Bhandary

  3. Department of Medicine, Columbia University, New York, NY, USA

    Edward J. Ciaccio

  4. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi, 599489, Singapore

    U. Rajendra Acharya

  5. Department of Biomedical Engineering, School of Science and Technology, SUSS University, Clementi, 599491, Singapore

    U. Rajendra Acharya

  6. School of Medicine, Faculty of Health and Medical Sciences, Taylor’s University, Subang Jaya, Malaysia

    U. Rajendra Acharya

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  1. U. Raghavendra
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  2. Anjan Gudigar
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Correspondence to Anjan Gudigar.

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Raghavendra, U., Gudigar, A., Bhandary, S.V. et al. A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images. J Med Syst 43, 299 (2019). https://doi.org/10.1007/s10916-019-1427-x

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