Smart Phone based Fundus Imaging for Diabetic Retinopathy Detection
DOI:
https://doi.org/10.4108/eetpht.9.4376Keywords:
Fundus Images, Smartphone base fundus imaging, diabetic retinopathy, deep learningAbstract
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.
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Chandakkar, P.S., Venkatesan, R., Li, B.: Retrieving clinically relevant diabetic retinopathy images using a multi-class multiple-instance framework. SPIE. 8670, 86700Q (2013). https://doi.org/10.1117/12.2008133. DOI: https://doi.org/10.1117/12.2008133
Bourne, R.R.A., Stevens, G.A., White, R.A., Smith, J.L., Flaxman, S.R., Price, H., Jonas, J.B., Keeffe, J., Leasher, J., Naidoo, K., Pesudovs, K., Resnikoff, S., Taylor, H.R.: Causes of vision loss worldwide, 1990-2010: A systematic analysis. Lancet Glob. Heal. 1, e339–e349 (2013). https://doi.org/10.1016/S2214-109X(13)70113-X. DOI: https://doi.org/10.1016/S2214-109X(13)70113-X
CDC: National Diabetes Statistics Report 2020. Estimates of diabetes and its burden in the United States. (2020).
Ramasamy, K., Raman, R., Tandon, M.: Current state of care for diabetic retinopathy in India. Curr. Diab. Rep. 13, 460–468 (2013). https://doi.org/10.1007/S11892-013-0388-6/METRICS. DOI: https://doi.org/10.1007/s11892-013-0388-6
Kashyap, N., Singh, D.K., Singh, G.K.: Mobile phone based diabetic retinopathy detection system using ANN-DWT. 2017 4th IEEE Uttar Pradesh Sect. Int. Conf. Electr. Comput. Electron. UPCON 2017. 2018-January, 463–467 (2017). https://doi.org/10.1109/UPCON.2017.8251092. DOI: https://doi.org/10.1109/UPCON.2017.8251092
WHO Global Report: Global Report on Diabetes. Isbn. 978, 11 (2016).
Barometer, D.R.: The Diabetic Retinopathy Barometer Report: Global Findings. (2017).
Taylor, R., Broadbent, D.M., Greenwood, R., Hepburn, D., Owens, D.R., Simpson, H.: Mobile retinal screening in Britain. In: Diabetic Medicine (1998). https://doi.org/10.1002/(SICI)1096-9136(199804)15:4<344::AID-DIA588>3.0.CO;2-O. DOI: https://doi.org/10.1002/(SICI)1096-9136(199804)15:4<344::AID-DIA588>3.0.CO;2-O
Haddock, L.J., Kim, D.Y., Mukai, S.: Simple, inexpensive technique for high-quality smartphone fundus photography in human and animal eyes. J. Ophthalmol. 2013, (2013). https://doi.org/10.1155/2013/518479. DOI: https://doi.org/10.1155/2013/518479
Hacisoftaoglu, R.E., Karakaya, M., Sallam, A.B.: Deep learning frameworks for diabetic retinopathy detection with smartphone-based retinal imaging systems. Pattern Recognit. Lett. 135, (2020). https://doi.org/10.1016/j.patrec.2020.04.009. DOI: https://doi.org/10.1016/j.patrec.2020.04.009
Majumder, S., Elloumi, Y., Akil, M., Kachouri, R., Kehtarnavaz, N.: A deep learning-based smartphone app for real-time detection of five stages of diabetic retinopathy. Presented at the (2020). https://doi.org/10.1117/12.2557554. DOI: https://doi.org/10.1117/12.2557554
Nunes, F., Madureira, P., Rego, S., Braga, C., Moutinho, R., Oliveira, T., Soares, F.: A Mobile Tele-Ophthalmology System for Planned and Opportunistic Screening of Diabetic Retinopathy in Primary Care. IEEE Access. 9, (2021). https://doi.org/10.1109/ACCESS.2021.3085404. DOI: https://doi.org/10.1109/ACCESS.2021.3085404
Alves, S.S.A., Matos, A.G., Almeida, J.S., Benevides, C.A., Cunha, C.C.H., Santiago, R.V.C., Pereira, R.F., Reboucas Filho, P.P.: A New strategy for the detection of diabetic retinopathy using a smartphone app and machine learning methods embedded on cloud computer. In: Proceedings - IEEE Symposium on Computer-Based Medical Systems (2020). https://doi.org/10.1109/CBMS49503.2020.00108. DOI: https://doi.org/10.1109/CBMS49503.2020.00108
Karakaya, M., Hacisoftaoglu, R.E.: Comparison of smartphone-based retinal imaging systems for diabetic retinopathy detection using deep learning. BMC Bioinformatics. 21, (2020). https://doi.org/10.1186/s12859-020-03587-2. DOI: https://doi.org/10.1186/s12859-020-03587-2
Wintergerst, M.W.M., Mishra, D.K., Hartmann, L., Shah, P., Konana, V.K., Sagar, P., Berger, M., Murali, K., Holz, F.G., Shanmugam, M.P., Finger, R.P.: Diabetic Retinopathy Screening Using Smartphone-Based Fundus Imaging in India. Ophthalmology. 127, (2020). https://doi.org/10.1016/j.ophtha.2020.05.025. DOI: https://doi.org/10.1016/j.ophtha.2020.05.025
Tymchenko, B., Marchenko, P., Spodarets, D.: Deep learning approach to diabetic retinopathy detection. In: ICPRAM 2020 - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods (2020). https://doi.org/10.5220/0008970805010509. DOI: https://doi.org/10.5220/0008970805010509
Gupta, S., Thakur, S., Gupta, A.: Optimized hybrid machine learning approach for smartphone based diabetic retinopathy detection. Multimed. Tools Appl. 81, (2022). https://doi.org/10.1007/s11042-022-12103-y. DOI: https://doi.org/10.1007/s11042-022-12103-y
Rajalakshmi, R., Subashini, R., Anjana, R.M., Mohan, V.: Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye 2018 326. 32, 1138–1144 (2018). https://doi.org/10.1038/s41433-018-0064-9. DOI: https://doi.org/10.1038/s41433-018-0064-9
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