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Diabetic retinopathy detection and diagnosis by means of robust and explainable convolutional neural networks

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Abstract

The diabetic retinopathy is a disease affecting the retina and it is currently manually diagnosed by specialists. In order to help the clinician in this time-consuming task, we propose a method aimed at automatically identify the diabetic retinopathy presence from ocular angiography by exploiting convolutional neural networks. In particular, two models are proposed: the first one is aimed to discriminate between healthy eyes and eyes with retinopathy, while the second one is designed to distinguish between non-proliferative retinopathy and weakly and severely proliferative retinopathy. The results we obtained, i.e., an accuracy of 0.98 for the first model and an accuracy of 0.91 relative to the second model, demonstrate that the proposed models can effectively aid the clinician in diagnosis. Moreover, the proposed method is aimed to localize the disease in the angiography, providing a kind of explainability behind the model diagnosis, by taking into account two different class activation mapping algorithms showing on the images the areas symptomatic of the disease, in order to increase model trustworthiness from doctors and patients. We also introduce a similarity index aimed to evaluate the model robustness by quantifying how much the heatmaps generated by the class activation mapping algorithms of the same model differ from each other.

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Data availability

The datasets considered in the experimental evaluation are freely available from the Kaggle repository: the first one is available inFootnote 5, while the second one in.Footnote 6 Both of them are freely available for research purposes. Furthermore, the Python code we developed is also available for research purposes at the following GitHub repository.Footnote 7

Notes

  1. https://www.iss.it/.

  2. https://www.iapb.org/.

  3. https://www.kaggle.com/c/diabetic-retinopathy-detection/data/.

  4. https://www.kaggle.com/competitions/aptos2019-blindness-detection.

  5. https://www.kaggle.com/c/diabetic-retinopathy-detection/data/.

  6. https://www.kaggle.com/competitions/aptos2019-blindness-detection.

  7. https://github.com/Djack1010/tami.

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Acknowledgements

This work has been partially supported by EU DUCA, EU CyberSecPro, EU E-CORRIDOR projects, PNRR SERICS_SPOKE1_DISE, RdS 2022–2024 cybersecurity and SERICS (PE00000014) under the MUR National Recovery and Resilience Plan funded by the European Union - NextGenerationEU.

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Authors and Affiliations

  1. Institute of Informatics and Telematics, National Research Council, Pisa, Italy

    Francesco Mercaldo, Marcello Di Giammarco, Giacomo Di Iadarola & Fabio Martinelli

  2. Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, Campobasso, Italy

    Francesco Mercaldo, Arianna Apicella & Antonella Santone

  3. Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy

    Mario Cesarelli

  4. Department of Information Engineering, University of Pisa, Pisa, Italy

    Marcello Di Giammarco

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  1. Francesco Mercaldo
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  2. Marcello Di Giammarco
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  4. Giacomo Di Iadarola
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  5. Mario Cesarelli
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Correspondence to Francesco Mercaldo.

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Mercaldo, F., Di Giammarco, M., Apicella, A. et al. Diabetic retinopathy detection and diagnosis by means of robust and explainable convolutional neural networks. Neural Comput & Applic 35, 17429–17441 (2023). https://doi.org/10.1007/s00521-023-08608-8

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  • DOI: https://doi.org/10.1007/s00521-023-08608-8

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