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Referable Diabetic Retinopathy Detection Using Deep Feature Extraction and Random Forest

  • Conference paper
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Biomedical Engineering Systems and Technologies (BIOSTEC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1814))

  • 218 Accesses

Abstract

Diabetic retinopathy (DR) is the most common eyes complication of diabetes worldwide; it can cause vision loss and blindness. The early diagnosis can significantly help in assuring an effective treatment. The computer vision techniques are playing an important role in improving the diagnosis results. This paper proposes a hybrid architecture that combines: four of the most recent deep learning techniques for feature extraction (DenseNet_201, MobileNet_V2, VGG16 and VGG19) with a random forest classifier for referable diabetic retinopathy detection over the APTOS, Kaggle DR and Messidor-2 datasets. The study evaluated and compared: (1) the random forest models with their base learners, (2) the designed random forest models with the same feature extractor over different number of trees, (3) the decision tree classifiers with the best random forest models and (4) the best random forest models of the four feature extractors to each other. The empirical evaluations used: four classification performance criteria (accuracy, sensitivity, precision and F1-score), 5-fold cross-validation, Scott Knott statistical test, and Borda Count voting method. The best model was constructed using a random forest classifier of 9 trees with MobileNet_V2 for feature extraction, it was trained over the APTOS dataset, and it achieved an accuracy value of 82.12%. The experimental results demonstrated that combining random forest with deep learning models is effective for referable diabetic retinopathy detection using fundus images.

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Authors and Affiliations

  1. Software Project Management Research Team, ENSIAS, Mohammed V University in Rabat, Rabat, Morocco

    Chaymaa Lahmar & Ali Idri

  2. Modeling, Simulation and Data Analysis, Mohammed VI Polytechnic University Benguerir, Ben Guerir, Morocco

    Ali Idri

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  1. Chaymaa Lahmar
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  2. Ali Idri
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Correspondence to Ali Idri .

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Editors and Affiliations

  1. Universidade Nova de Lisboa, Caparica, Portugal

    Ana Cecília A. Roque

  2. Virginia Tech, Blacksburg, VA, USA

    Denis Gracanin

  3. University of Vienna, Vienna, Austria

    Ronny Lorenz

  4. University of Edinburgh, Edinburgh, UK

    Athanasios Tsanas

  5. Université de Montréal, Montréal, QC, Canada

    Nathalie Bier

  6. Instituto de Telecomunicações and Instituto Superior Técnico - Lisbon University, Lisbon, Portugal

    Ana Fred

  7. Nova School of Science and Technology and University of Lisbon, Caparica, Portugal

    Hugo Gamboa

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Lahmar, C., Idri, A. (2023). Referable Diabetic Retinopathy Detection Using Deep Feature Extraction and Random Forest. In: Roque, A.C.A., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2022. Communications in Computer and Information Science, vol 1814. Springer, Cham. https://doi.org/10.1007/978-3-031-38854-5_21

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