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Is Diabetic Retinopathy Grading Biased by Imbalanced Datasets?

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Optimization, Learning Algorithms and Applications (OL2A 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1754))

Abstract

Diabetic retinopathy (DR) is one of the most severe complications of diabetes and the leading cause of vision loss and even blindness. Retinal screening contributes to early detection and treatment of diabetic retinopathy. This eye disease has five stages, namely normal, mild, moderate, severe and proliferative diabetic retinopathy. Usually, highly trained ophthalmologists are capable of manually identifying the presence or absence of retinopathy in retinal images. Several automated deep learning (DL) based approaches have been proposed and they have been proven to be a powerful tool for DR detection and classification. However, these approaches are usually biased by the cardinality of each grade set, as the overall accuracy benefits the largest sets in detriment of smaller ones. In this paper, we applied several state-of-the-art DL approaches, using a 5-fold cross-validation technique. The experiments were conducted on a balanced DDR dataset containing 31330 retina fundus images by completing the small grade sets with samples from other well known datasets. This balanced dataset increases robustness of training and testing tasks as they used samples from several origins and obtained with different equipment. The results confirm the bias introduced by using imbalanced datasets in automatic diabetic retinopathy grading.

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Acknowledgments

The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).

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Authors and Affiliations

  1. Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal

    Fernando C. Monteiro & José Rufino

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  1. Fernando C. Monteiro
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  2. José Rufino
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Correspondence to Fernando C. Monteiro .

Editor information

Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. University of Ljubljana, Ljubljana, Slovenia

    Andrej Košir

  3. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  5. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Teixeira

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    Rui P. Lopes

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Monteiro, F.C., Rufino, J. (2022). Is Diabetic Retinopathy Grading Biased by Imbalanced Datasets?. In: Pereira, A.I., Košir, A., Fernandes, F.P., Pacheco, M.F., Teixeira, J.P., Lopes, R.P. (eds) Optimization, Learning Algorithms and Applications. OL2A 2022. Communications in Computer and Information Science, vol 1754. Springer, Cham. https://doi.org/10.1007/978-3-031-23236-7_4

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  • DOI: https://doi.org/10.1007/978-3-031-23236-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23235-0

  • Online ISBN: 978-3-031-23236-7

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