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Automatic Glaucoma Diagnosis in Digital Fundus Images Using Deep CNNs

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Advances in Computational Intelligence Techniques

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

The World Health Organization (WHO) approximates that more than 42 million people are currently blind in the world, 80 per cent of which could have been prevented or cured by early detection. According to a survey, glaucoma is the second most leading cause for blindness after cataract. It is an irreversible eye disease, and once the vision is lost, it cannot be recovered. Thus, it is vital to develop an automatic computerized tool to diagnose the disease. In this paper, a novel and robust deep learning-based convolutional neural networks (CNNs) architecture has been proposed to deal with the problem. The network consists of six convolutional layers, with various activation functions, and pooling layers to get the abstract and detailed information of the input image. The proposed architecture predicts the probability of an image being glaucoma. The model has been experimented with Refugee and Drishti datasets. Our proposed model is able to diagnose the glaucoma disease automatically with an accuracy of 90%, sensitivity of 96%, and specificity of 84%, respectively.

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Author information

Authors and Affiliations

  1. Bharti School of Telecommunication Technology and Management (BSTTM), IITD, New Delhi, India

    Ambika Sharma

  2. Centre for Applied, New Delhi, India

    Monika Agrawal

  3. Electrical Department, IITD, New Delhi, India

    Sumantra Dutta Roy

  4. AIIMS Delhi, New Delhi, India

    Vivek Gupta

Authors
  1. Ambika Sharma
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  2. Monika Agrawal
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  3. Sumantra Dutta Roy
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  4. Vivek Gupta
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Corresponding author

Correspondence to Ambika Sharma .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India

    Shruti Jain

  2. National Institute of Technical Teachers Training and Research, Chandigarh, India

    Meenakshi Sood

  3. Department of Biomedical Engineering, North-Eastern Hill University, Shillong, Meghalaya, India

    Sudip Paul

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Sharma, A., Agrawal, M., Roy, S.D., Gupta, V. (2020). Automatic Glaucoma Diagnosis in Digital Fundus Images Using Deep CNNs. In: Jain, S., Sood, M., Paul, S. (eds) Advances in Computational Intelligence Techniques. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-2620-6_3

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