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A Systematic Review of Transfer Learning-Based Approaches for Diabetic Retinopathy Detection

Year 2023, Volume: 36 Issue: 3, 1140 - 1157, 01.09.2023
https://doi.org/10.35378/gujs.1081546

Abstract

Diabetic retinopathy, which is extreme visual blindness due to diabetes, has become an alarming issue worldwide. Early and accurate detection of DR is necessary to prevent the progression and reduce the risk of blindness. Recently, many approaches for DR detection have been proposed in the literature. Among them, deep neural networks (DNNs), especially Convolutional Neural Network (CNN) models, have become the most offered approach. However, designing and training new CNN architectures from scratch is a troublesome and labor-intensive task, particularly for medical images. Moreover, it requires training tremendous amounts of parameters. Therefore, transfer learning approaches as pre-trained models have become more prevalent in the last few years. Accordingly, in this study, 43 publications based on DNN and Transfer Learning approaches for DR detection between 2016 and 2021 are reviewed. The reviewed papers are summarized in 4 figures and 10 tables that present detailed information about 29 pre-trained CNN models, 13 DR data sets, and standard performance metrics. 

References

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Year 2023, Volume: 36 Issue: 3, 1140 - 1157, 01.09.2023
https://doi.org/10.35378/gujs.1081546

Abstract

References

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  • [21] Wu J.-H., Liu T. Y. A., Hsu W.-T., Ho J. H.-C., and Lee C.-C., “Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis”, Journal of medical Internet research, vol. 23(7): e23863, (2021).
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  • [23] Wang, X., Lu, Y., Wang, Y., AND Chen, W. B., “Diabetic retinopathy stage classification using convolutional neural networks.”, 2018 IEEE International Conference on Information Reuse and Integration (IRI), 465-471, (2018).
  • [24] Wan, S., Liang, Y., Zhang, Y., “Deep convolutional neural networks for diabetic retinopathy detection by image classification”, Computers and Electrical Engineering, 72: 274–282, (2018).
  • [25] Chouhan, V., Singh, S. K., Khamparia, A., Gupta, D., Tiwari, P., Moreira, C., Damaševičius, R., and de Albuquerque, V. H. C., “A novel transfer learning based approach for pneumonia detection in chest X-ray images”, Applied Sciences (Switzerland), 10(2), (2020).
  • [26] Ul Abideen, Z., Ghafoor, M., Munir, K., Saqib, M., Ullah, A., Zia, T., Tariq, S. A., Ahmed, G., and Zahra, A., “Uncertainty assisted robust tuberculosis identification with bayesian convolutional neural networks”, IEEE Access, 8:22812–22825, (2020).
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  • [28] Russakovsky O., Deng J., Su H., Krause J., Satheesh S., Ma S., Huang Z., Karpathy A., Khosla A., Bernstein M., Berg Alexander C. and Li Fei-Fei, “Imagenet large scale visual recognition challenge”, Int J Comput Vis, 115: 211–252, (2015).
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  • [30] Lam, C., Yi, D., Guo, M., and Lindsey, T., “Automated detection of diabetic retinopathy using deep learning.”, AMIA summits on translational science proceedings, 2018: 147, (2018).
  • [31] Xu, X., Lin, J., Tao, Y., & Wang, X., “An improved DenseNet method based on transfer learning for fundus medical images”, 2018 7th International Conference on Digital Home (ICDH), 137–140, (2018).
  • [32] Hathwar, S. B., and Srinivasa, G., “Automated grading of diabetic retinopathy in retinal fundus images using deep learning”, 2019 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 73–77, (2019).
  • [33] Kassani, S. H., “Diabetic retinopathy classification using a modified Xception architecture”, IEEE international symposium on signal processing and information technology (ISSPIT), 1-6, (2019).
  • [34] Wijesinghe, I., Gamage, C., and Chitraranjan, C., “Transfer learning with ensemble feature extraction and low-rank matrix factorization for severity stage classification of diabetic retinopathy”, IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 931–938, (2019).
  • [35] Li, T., Gao, Y., Wang, K., Guo, S., Liu, H., and Kang, H., “Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening”, Information Sciences, 501: 511–522, (2019).
  • [36] Ahmad, M., Kasukurthi, N., and Pande, H., “Deep learning for weak supervision of diabetic retinopathy abnormalities”, IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 573–577, (2019).
  • [37] Math, L. and Fatima, R., “Identification of diabetic retinopathy from fundus images using CNNs”, International Journal of Innovative Technology and Exploring Engineering, 9(1): 3439–3443, (2019).
  • [38] Yip, M. Y. T., Lim, Z. W., Lim, G., Quang, N. D., Hamzah, H., Ho, J., ... and Ting, D. S. W., “Enhanced detection of referable diabetic retinopathy via DCNNs and transfer learning”, Asian Conference on Computer Vision, Springer, Cham., 282-288, (2019).
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  • [41] Islam, N., Saeed, U., Naz, R., Tanveer, J., Kumar, K., and Shaikh, A. A., “DeepDR: An image guided diabetic retinopathy detection technique using attention-based deep learning scheme”, 2nd International Conference on new Trends in Computing Sciences (ICTCS) 2019, 1-6, (2019).
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There are 72 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Electrical & Electronics Engineering
Authors

Burcu Oltu 0000-0002-6980-6235

Büşra Kübra Karaca 0000-0002-5901-8243

Hamit Erdem 0000-0003-1704-1581

Atilla Özgür 0000-0002-9237-8347

Publication Date September 1, 2023
Published in Issue Year 2023 Volume: 36 Issue: 3

Cite

APA Oltu, B., Karaca, B. K., Erdem, H., Özgür, A. (2023). A Systematic Review of Transfer Learning-Based Approaches for Diabetic Retinopathy Detection. Gazi University Journal of Science, 36(3), 1140-1157. https://doi.org/10.35378/gujs.1081546
AMA Oltu B, Karaca BK, Erdem H, Özgür A. A Systematic Review of Transfer Learning-Based Approaches for Diabetic Retinopathy Detection. Gazi University Journal of Science. September 2023;36(3):1140-1157. doi:10.35378/gujs.1081546
Chicago Oltu, Burcu, Büşra Kübra Karaca, Hamit Erdem, and Atilla Özgür. “A Systematic Review of Transfer Learning-Based Approaches for Diabetic Retinopathy Detection”. Gazi University Journal of Science 36, no. 3 (September 2023): 1140-57. https://doi.org/10.35378/gujs.1081546.
EndNote Oltu B, Karaca BK, Erdem H, Özgür A (September 1, 2023) A Systematic Review of Transfer Learning-Based Approaches for Diabetic Retinopathy Detection. Gazi University Journal of Science 36 3 1140–1157.
IEEE B. Oltu, B. K. Karaca, H. Erdem, and A. Özgür, “A Systematic Review of Transfer Learning-Based Approaches for Diabetic Retinopathy Detection”, Gazi University Journal of Science, vol. 36, no. 3, pp. 1140–1157, 2023, doi: 10.35378/gujs.1081546.
ISNAD Oltu, Burcu et al. “A Systematic Review of Transfer Learning-Based Approaches for Diabetic Retinopathy Detection”. Gazi University Journal of Science 36/3 (September 2023), 1140-1157. https://doi.org/10.35378/gujs.1081546.
JAMA Oltu B, Karaca BK, Erdem H, Özgür A. A Systematic Review of Transfer Learning-Based Approaches for Diabetic Retinopathy Detection. Gazi University Journal of Science. 2023;36:1140–1157.
MLA Oltu, Burcu et al. “A Systematic Review of Transfer Learning-Based Approaches for Diabetic Retinopathy Detection”. Gazi University Journal of Science, vol. 36, no. 3, 2023, pp. 1140-57, doi:10.35378/gujs.1081546.
Vancouver Oltu B, Karaca BK, Erdem H, Özgür A. A Systematic Review of Transfer Learning-Based Approaches for Diabetic Retinopathy Detection. Gazi University Journal of Science. 2023;36(3):1140-57.