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Explainable Diabetic Retinopathy using EfficientNET.

Mohamed Chetoui, Moulay A Akhloufi

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
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    Deep learning using EfficientNet effectively detects diabetic retinopathy (DR), including vision-threatening stages. This AI approach shows high accuracy in identifying DR signs, aiding timely treatment for diabetes mellitus patients.

    Area of Science:

    • Ophthalmology
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Diabetic retinopathy (DR) is a complication of diabetes mellitus, potentially leading to vision loss.
    • Early detection of DR is crucial to prevent severe visual impairment or blindness.

    Purpose of the Study:

    • To propose a deep learning model for detecting referable diabetic retinopathy (RDR) and vision-threatening DR.
    • To evaluate the model's performance on public datasets.

    Main Methods:

    • Utilized a deep learning architecture based on the EfficientNet convolutional neural network.
    • Tested the model on the EyePACS and APTOS 2019 public datasets.
    • Incorporated an explainability algorithm to identify DR signs.

    Main Results:

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    • Achieved state-of-the-art performance on both datasets.
    • Obtained high Area Under Curve (AUC) scores: 0.984 for RDR and 0.990 for vision-threatening DR on EyePACS.
    • Achieved AUC scores of 0.966 for RDR and 0.998 for vision-threatening DR on APTOS 2019.

    Conclusions:

    • The proposed EfficientNet-based deep learning model demonstrates high efficacy in detecting diabetic retinopathy.
    • The model's explainability confirms its ability to identify key DR indicators.
    • This AI approach offers a promising tool for early and accurate DR diagnosis.