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Automated diabetic retinopathy screening using deep learning

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Abstract

The purpose of this research is to propose a new method for identifying diabetic retinopathy using retinal fundus images. Currently, identifying diabetic retinopathy from computerized fundus images is a challenging task in medical image processing and requires new strategies to be developed. The manual analysis of the retinal fundus is time-consuming and requires a significant amount of skill. To assist clinicians, this research develops a graphical user interface that integrates imaging algorithms to assess whether the patient’s fundus image is affected by diabetic retinopathy. The diagnosis is made using a deep neural network, specifically the Resnet152-V2, which has been shown to have 100% accuracy in all evaluation criteria including accuracy, recall, precision, and F1 Score. The severity of the disease is displayed on the graphical user interface and the patient’s information is stored in a local database. This proposed method can also be used by ophthalmologists as a backup option to support in disease detection, reducing the necessary processing time.

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Data availability

The dataset used in this study is public and can be found at the following links: https://www.kaggle.com/datasets/sovitrath/diabetic-retinopathy-224x224-2019-data.

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Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

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Authors and Affiliations

  1. Faculty of Engineering, Uni de Moncton, Moncton, NB, E1A3E9, Canada

    Sarra Guefrachi & Habib Hamam

  2. Advanced Technologies for Medicine and Signal Laboratory ‘ATMS’, National Engineering School of Sfax (ENIS), Sfax University, Sfax, Tunisia

    Amira Echtioui

  3. College of Computer Science and Engineering, University of Ha’il, Ha’il, 55476, Saudi Arabia

    Habib Hamam

  4. International Institute of Technology and Management (IITG), Avenue des Grandes Ecoles, P.O. Box 1989, Libreville, Gabon

    Habib Hamam

  5. Spectrum of Knowledge Production & Skills Development, P.O. Box 3027, Sfax, Tunisia

    Habib Hamam

  6. Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, Johannesburg, 2006, South Africa

    Habib Hamam

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  1. Sarra Guefrachi
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  2. Amira Echtioui
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  3. Habib Hamam
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Contributions

Conceptualization was done by SG and AE. All the literature reading and data gathering were performed by SG. All the experiments and coding were performed by SG and AE. The formal analysis was performed by AE. Manuscript writing original draft preparation was done by SG and AE. Review and editing were done by SG, AE, HH. Visualization work was carried out by SG, AE, HH.

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Correspondence to Amira Echtioui.

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Guefrachi, S., Echtioui, A. & Hamam, H. Automated diabetic retinopathy screening using deep learning. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18149-4

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