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Effect of color information on the diagnostic performance of glaucoma in deep learning using few fundus images

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Abstract

Purpose

The purpose of this study was to evaluate the accuracy of the convolutional neural network (CNN) model in glaucoma identification with three primary colors (red, green, blue; RGB) and split color channels using fundus photographs with a small sample size.

Methods

The dataset was prepared using color fundus photographs captured with a fundus camera (VX-10i, Kowa Co., Ltd., Tokyo, Japan). The training dataset consisted of 200 images, and the validation dataset contained 60 images. In the preprocessing stage, the color channels for the fundus images were separated into red (red model), green (green model), and blue (blue model) using OpenCV on Windows. All images were resized to squares with a size of 512 × 512 pixels for preprocessing before input into the model, and the model was fine-tuned with VGG16.

Results

The diagnostic performance was significantly higher in the green model [area under the curve (AUC) 0.946; 95% confidence interval (CI) 0.851–0.982] than in the RGB model (AUC 0.800; 95% CI 0.658–0.893; P = 0.006), red model (AUC 0.746; 95% CI 0.601–0.851; P = 0.002), and blue model (AUC 0.558; 95% CI 0.405–0.700; P < 0.001).

Conclusion

The present study showed that the green digital filter is useful for structuring CNN models for automatic discrimination of glaucoma using fundus photographs with a small sample size. The present findings suggest that preprocessing, when creating the CNN model, is an important step for the identification of a large number of retinal diseases using color fundus photographs.

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Acknowledgements

This study was supported by grants from Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research (KAKENHI multi-year Fund), Grant Number of 19K20728 and Santen Pharmaceutical (Grant No. 6).

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Authors and Affiliations

  1. Department of Orthoptics, Faculty of Medical Technology, Teikyo University, Itabashi, Tokyo, Japan

    Masakazu Hirota & Takao Hayashi

  2. Department of Ophthalmology, School of Medicine, Teikyo University, 2-11-1 Kaga, Itabashi-ku, Tokyo, 173-8605, Japan

    Masakazu Hirota, Atsushi Mizota, Tatsuya Mimura & Takao Hayashi

  3. Division of Clinical Radiology, Graduate School of Medical Care and Technology, Teikyo University, Itabashi, Tokyo, Japan

    Junichi Kotoku

  4. Medical Information Systems Research Center, Teikyo University, Itabashi, Tokyo, Japan

    Tomohiro Sawa

  5. Inoue Eye Hospital, Chiyoda, Tokyo, Japan

    Kenji Inoue

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  1. Masakazu Hirota
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Contributions

Substantial contributions are as follows: MH, AM contributed for the conception and design; KI for the acquisition of data; MH for analysis and interpretation of data and drafting the article or revising it critically for important intellectual content. MH, AM, TM, TH, JK, TS, KI contributed for the final approval of the version to be published, and MH, AM, TM, TH, JK, TS, KI for the agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding author

Correspondence to Masakazu Hirota.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This investigation adheres to the tenets of the World Medical Association Declaration of Helsinki. The experimental protocols and consent procedures were approved by the Institutional Review Board of Teikyo University (approval no. 18-161).

Informed consent

Informed consent was obtained from all subjects after explaining the nature and possible complications of the study.

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Hirota, M., Mizota, A., Mimura, T. et al. Effect of color information on the diagnostic performance of glaucoma in deep learning using few fundus images. Int Ophthalmol 40, 3013–3022 (2020). https://doi.org/10.1007/s10792-020-01485-3

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  • DOI: https://doi.org/10.1007/s10792-020-01485-3

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