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Diabetic Retinopathy Detection Using Multi-layer Neural Networks and Split Attention with Focal Loss

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Neural Information Processing (ICONIP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12534))

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Abstract

Diabetic retinopathy (DR) is the most common eye threatening micro-vascular complication of diabetes. It develops and grows without arbitrary symptoms and can ultimately lead to blindness. However, 90% of the DR-attributed blindness is preventable but needs prompt diagnosis and appropriate treatment. Presently, DR detection is time and resource-consuming, i.e., required qualified ophthalmologist technician to examine the retina colour fundus for investigating the existence of vascular anomaly associated lesions. Nevertheless, an automatic DR scanning with specialised deep learning algorithms can overcome this challenge. In this paper, we present an automatic detection of DR using Multi-layer Neural Networks and Split Attention with Focal Loss. Our method outperformed state-of-the-art (SOTA) networks in early-stage detection and achieved 85.9% accuracy in DR classification. Because of high performance, it is believed that the results obtained in this paper are of great importance to the medical and the relevant research community.

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Author information

Authors and Affiliations

  1. School of Computer Science, University of Sydney, Sydney, Australia

    Usman Naseem, Matloob Khushi & Simon K. Poon

  2. School of Engineering, RMIT University, Melbourne, Australia

    Shah Khalid Khan

  3. School of Electrical Engineering, University of Technology Sydney, Sydney, Australia

    Nazar Waheed

  4. School of Computer Sciences, Western Sydney University, Sydney, Australia

    Adnan Mir

  5. Centre for Lifelong Learning, Universiti Brunei Darussalam, Gadong, Brunei

    Atika Qazi

  6. College of Computer and Information Sciences, Jouf University, Sakakah, Saudi Arabia

    Bandar Alshammari

Authors
  1. Usman Naseem
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  2. Matloob Khushi
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  3. Shah Khalid Khan
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  4. Nazar Waheed
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  5. Adnan Mir
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  6. Atika Qazi
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  7. Bandar Alshammari
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  8. Simon K. Poon
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Corresponding author

Correspondence to Usman Naseem .

Editor information

Editors and Affiliations

  1. Department of AI, Ping An Life, Shenzhen, China

    Haiqin Yang

  2. Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Kitsuchart Pasupa

  3. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi-Sing Leung

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    James T. Kwok

  5. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

  6. The Chinese University of Hong Kong, New Territories, Hong Kong

    Irwin King

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Naseem, U. et al. (2020). Diabetic Retinopathy Detection Using Multi-layer Neural Networks and Split Attention with Focal Loss. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12534. Springer, Cham. https://doi.org/10.1007/978-3-030-63836-8_3

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  • DOI: https://doi.org/10.1007/978-3-030-63836-8_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63835-1

  • Online ISBN: 978-3-030-63836-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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