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Research Article

Smart Phone based Fundus Imaging for Diabetic Retinopathy Detection

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  • @ARTICLE{10.4108/eetpht.9.4376,
        author={Adarsh Benjamin and Farha Fatina Wahid and Jenefa J},
        title={Smart Phone based Fundus Imaging for Diabetic Retinopathy Detection},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={9},
        number={1},
        publisher={EAI},
        journal_a={PHAT},
        year={2023},
        month={11},
        keywords={Fundus Images, Smartphone base fundus imaging, diabetic retinopathy, deep learning},
        doi={10.4108/eetpht.9.4376}
    }
    
  • Adarsh Benjamin
    Farha Fatina Wahid
    Jenefa J
    Year: 2023
    Smart Phone based Fundus Imaging for Diabetic Retinopathy Detection
    PHAT
    EAI
    DOI: 10.4108/eetpht.9.4376
Adarsh Benjamin1, Farha Fatina Wahid2,*, Jenefa J3
  • 1: CMR Institute of Technology
  • 2: Kannur University
  • 3: Christ University
*Contact email: farhawahid@gmail.com

Abstract

INTRODUCTION: Diabetic retinopathy (DR) is one of the consequences of diabetes which if untreated may lead to loss of vision. Generally, for DR detection, retinal images are obtained using a traditional fundus camera. A recent trend in the acquisition of eye fundus images is the usage of smartphones to acquire images. OBJECTIVES: This paper focuses on the study of existing works which incorporated smartphones for obtaining fundus images and various devices available in the market. Also, the common datasets used for carrying out DR detection using smartphone-based fundus images as well as the classification models used for the diagnosis of DR are explored. METHODS: A search of information was carried out on articles based on DR detection from fundus images published in the state-of-the-art literatures. RESULTS: Majority of the works uses SBFI devices like 20D lens, EyeExaminer etc. to obtain fundus image. The common databases used for the study are EyePACS, Messidor, etc. and the classification models mostly rely on deep learning frameworks. CONCLUSION: The use of smartphones for capturing fundus images for DR detection are explored. Smartphone devices, datasets used for the study and currently available classification models for SBFI based DR detection are discussed in detail. This paper portrays various approaches currently being employed in SBFI based DR detection.

Keywords
Fundus Images, Smartphone base fundus imaging, diabetic retinopathy, deep learning
Received
2023-09-19
Accepted
2023-11-05
Published
2023-11-13
Publisher
EAI
http://dx.doi.org/10.4108/eetpht.9.4376

Copyright © 2023 A. Benjamin et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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