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

Year 2023, , 1140 - 1157, 01.09.2023

Burcu Oltu , Büşra Kübra Karaca , Hamit Erdem , Atilla Özgür

https://doi.org/10.35378/gujs.1081546

Cited By: 3

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. 

Keywords

Deep learning, Convolutional neural networks, Diabetic retinopathy, Transfer learning

References

• [1] Karaca, B. K., Oltu B., Ozgur A., and Erdem H., “Comparison of Transfer Learning Strategies for Diabetic Retinopathy Detection”, 2021 Innovations in Intelligent Systems and Applications Conference (ASYU), 1–5, (2021).
• [2] https://www.who.int/features/factfiles/diabetes/en/ Access date: 21.02.2021.
• [3] Guariguata, L., Whiting, D. R., Hambleton, I., Beagley, J., Linnenkamp, U., and Shaw, J. E., “Global estimates of diabetes prevalence for 2013 and projections for 2035”, Diabetes Research and Clinical Practice, 103(2): 137–149, (2014).
• [4] Lakshminarayanan, V., Kheradfallah, H., Sarkar, A., and Jothi Balaji, J., “Automated detection and diagnosis of diabetic retinopathy: A comprehensive survey”, Journal of Imaging, 7(9): 165, (2021).
• [5] Li, F., Liu, Z., Chen, H., Jiang, M., Zhang, X., and Wu, Z., “Automatic detection of diabetic retinopathy in retinal fundus photographs based on deep learning algorithm”, Translational Vision Science and Technology, 8(6), (2019).
• [6] Alyoubi, W. L., Shalash, W. M., and Abulkhair, M. F., “Diabetic retinopathy detection through deep learning techniques: A review”, Informatics in Medicine Unlocked, 20: 100377, (2020).
• [7] Safi, H., Safi, S., Hafezi-Moghadam, A., and Ahmadieh, H. “Early detection of diabetic retinopathy”, Survey of Ophthalmology, 63(5): 601–608, (2018).
• [8] Ishtiaq, U., Abdul Kareem, S., Abdullah, E. R. M. F., Mujtaba, G., Jahangir, R., and Ghafoor, H. Y., “Diabetic retinopathy detection through artificial intelligent techniques: a review and open issues”, Multimedia Tools and Applications, 79(21–22): 15209–15252, (2020).
• [9] Lecun, Y., Bengio, Y., and Hinton, G., “Deep learning”, Nature, 521(7553): 436–444, (2015).
• [10] Tombaloğlu, B., And Erdem, H., “Turkish Speech Recognition Techniques and Applications of Recurrent Units (LSTM and GRU)”, Gazi University Journal of Science, 34 (4): 1035-1049, (2021).
• [11] Shamshirband S., Fathi M., Dehzangi A., Chronopoulos A. T., and Alinejad-Rokny H., “A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues”, J. Biomed. Inform., 113:103627, (2021).
• [12] Lin, D., Vasilakos, A. V., Tang, Y., and Yao, Y., “Neural networks for computer-aided diagnosis in medicine: A review”, Neurocomputing, 216: 700–708, (2016).
• [13] Asiri, N., Hussain, M., Al Adel, F., and Alzaidi, N., “Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey”, Artificial Intelligence in Medicine, 99:101701, (2019).
• [14] Sarki, R., Ahmed, K., Wang, H., and Zhang, Y., “Automatic detection of diabetic eye disease through deep learning using fundus images: A survey”, IEEE Access, 8: 151133–151149, (2020).
• [15] Nielsen, K. B., Lautrup, M. L., Andersen, J. K. H., Savarimuthu, T. R., and Grauslund, J., “Deep learning–based algorithms in screening of diabetic retinopathy: a systematic review of diagnostic performance”, Ophthalmology Retina, 3(4): 294–304, (2019).
• [16] Kandel, I., and Castelli, M., “Transfer learning with convolutional neural networks for diabetic retinopathy image classification. A review”, Applied Sciences, 10(6): 2021, (2020).
• [17] Chu, A., Squirrell, D., Phillips, A. M., and Vaghefi, E., “Essentials of a robust deep learning system for diabetic retinopathy screening: A systematic literature review”, Journal of Ophthalmology, (2020).
• [18] Stolte, S., and Fang, R., “A survey on medical image analysis in diabetic retinopathy”, Medical Image Analysis, 64: 101742, (2020).
• [19] Badar, M., Haris, M., and Fatima, A., “Application of deep learning for retinal image analysis: A review”, Computer Science Review, 35:100203, (2020).
• [20] Tsiknakis, N., Theodoropoulos, D., Manikis, G., Ktistakis, E., Boutsora, O., Berto, A., Scarpa, F., Scarpa, A., Fotiadis, D. I., and Marias, K., “Deep learning for diabetic retinopathy detection and classification based on fundus images: A review”, Computers in Biology and Medicine, 135: 104599, (2021).
• [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).
• [22] Uman, L. S., “Systematic reviews and meta-analyses”, Journal of the Canadian Academy of Child and Adolescent Psychiatry, 20(1): 57, (2007).
• [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).
• [27] Kieu, S. T. H., Bade, A., Hijazi, M. H. A., and Kolivand, H., “A survey of deep learning for lung disease detection on medical images: State-of-the-art, taxonomy, issues and future directions”, Journal of Imaging, 6(12): 131, (2020).
• [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).
• [29] Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., Hasan, M., Van Essen, B. C., Awwal, A. A. S., and Asari, V. K., “A state-of-the-art survey on deep learning theory and architectures”, Electronics (Switzerland), 8(3): 1–67, (2019).
• [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).
• [39] Dekhil, O., Naglah A., Shaban, M., Ghazal, M., Taher, F., and Elbaz, A., “Deep learning based method for computer aided diagnosis of diabetic retinopathy”, IEEE International Conference on Imaging Systems and Techniques (IST), 1–4, (2019).
• [40] Vo, H. H., and Verma, A., “New deep neural nets for fine-grained diabetic retinopathy recognition on hybrid color space”, IEEE International Symposium on Multimedia (ISM), 209-215, (2016).
• [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).
• [42] Hagos, M. T. and Kant ,S., “Transfer learning based detection of diabetic retinopathy from small dataset”, arXiv preprint arXiv: 1905.07203, (2019).
• [43] Voets, M., Møllersen, K., and Bongo, L. A., “Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs”, PLoS One, 14(6): 1–11, (2019).
• [44] Zhang, W., Zhong, J., Yang, S., Gao, Z., Hu, J., Chen, Y., and Yi, Z., “Automated identification and grading system of diabetic retinopathy using deep neural networks”, Knowledge-Based Systems, 175: 12–25, (2019).
• [45] Bodapati, J. D., Veeranjaneyulu, N., Shareef, S. N., Hakak, S., Bilal, M., Maddikunta, P. K. R., and Jo, O., “Blended multi-modal deep convnet features for diabetic retinopathy severity prediction”, Electronics (Switzerland), 9(6): 1–16, (2020).
• [46] Mateen, M., Wen, J., Nasrullah, N., Sun, S., and Hayat, S., “Exudate detection for diabetic retinopathy using pretrained convolutional neural networks”, Complexity, 1–11, (2020).
• [47] Roshan, S. M., Karsaz, A., Vejdani, A. H., and Roshan, Y. M., “Fine-tuning of pre-trained convolutional neural networks for diabetic retinopathy screening: a clinical study”, International Journal of Computational Science and Engineering, 21(4): 564–573, (2020).
• [48] Yu, Y., Chen, X., Zhu, X., Zhang, P., Hou, Y., Zhang, R., and Wu, C., “Performance of deep transfer learning for detecting abnormal fundus images”, Journal of Current Ophthalmology, 32(4): 368, (2020).
• [49] Hacisoftaoglu, R. E., Karakaya, M., and Sallam, A. B., “Deep learning frameworks for diabetic retinopathy detection with smartphone-based retinal imaging systems”, Pattern recognition letters, 135: 409–417, (2020).
• [50] Chen, P. N., Lee, C. C., Liang, C. M., Pao, S. I., Huang, K. H., and Lin, K. F. “General deep learning model for detecting diabetic retinopathy”, BMC Bioinformatics, 22(5): 1-15, (2021).
• [51] Choi, J. Y., Yoo, T. K., Seo, J. G., Kwak, J., Um, T. T., and Rim, T. H., “Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database”, PLoS One, 12(11): 1–16, (2017).
• [52] Somasundaram, K., Sivakumar, P., and Suresh, D., “Classification of diabetic retinopathy disease with transfer learning using deep convolutional neural networks”, Advances in Electrical and Computer Engineering, 21(3): 49-56, (2021).
• [53] Lam, C., Yu, C., Huang, L., and Rubin, D., “Retinal lesion detection with deep learning using image patches”, Investigative ophthalmology & visual science, 59(1): 590-596, (2018).
• [54] Wang, J., Yang, L., Huo, Z., He, W., and Luo, J., “Multi-Label classification of fundus images with EfficientNet”, IEEE Access, 8: 212499–212508, (2020).
• [55] Li, X., Pang, T., Xiong, B., Liu, W., Liang, P., and Wang, T., “Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification”, 10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), 1–11, (2018).
• [56] Zhou, L., Zhao, Y., Yang, J., Yu, Q., and Xu, X., “Deep multiple instance learning for automatic detection of diabetic retinopathy in retinal images”, IET Image Processing, 12(4): 563-571, (2018).
• [57] Lian, C., Liang, Y., Kang, R., and Xiang, Y., “Deep convolutional neural networks for diabetic retinopathy classification”, Proceedings of the 2nd International Conference on Advances in Image Processing, 68–72, (2018).
• [58] Gao, Z., Li, J., Guo, J., Chen, Y., Yi, Z., and Zhong, J., “Diagnosis of Diabetic Retinopathy Using Deep Neural Networks”, IEEE Access, 7: 3360–3370, (2019).
• [59] Mohammadian, S., Karsaz, A., and Roshan, Y. M., “Comparative study of fine-tuning of pre-trained convolutional neural networks for diabetic retinopathy screening”, 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME), 1–6, (2018).
• [60] Takahashi, H., Tampo, H., Arai, Y., Inoue, Y., and Kawashima, H., “Applying artificial intelligence to disease staging : Deep learning for improved staging of diabetic retinopathy”, PloS one, 12(6): 1–11, (2017).
• [61] Chen, H., Zeng, X., Luo, Y., and Ye, W., “Detection of diabetic retinopathy using deep neural network”, IEEE 23rd International Conference on Digital Signal Processing (DSP), 1-5, (2019).
• [62] Khan, Z., Khan, F. G., Khan, A., Rehman, Z. U., Shah, S., Qummar, S., ... and Pack, S.., “Diabetic retinopathy detection using VGG-NIN a deep learning architecture”, IEEE Access, 9: 61408–61416, (2021).
• [63] Saeed, F., Hussain, M., and Aboalsamh, H. A., “Automatic diabetic retinopathy diagnosis using adaptive fine-tuned convolutional neural network”, IEEE Access, 9: 41344–41359, (2021).
• [64] Yi, S. L., Yang, X. L., Wang, T. W., She, F. R., Xiong, X., and He, J. F., “Diabetic retinopathy diagnosis based on RA-Efficientnet”, Applied Sciences, 11(22): 11035, (2021).
• [65] Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... and Webster, D. R., “Development and validation of a deep learning algorithm detection of diabetic retinopathy in retinal fundus photographs”, Jama, 316(22): 2402-2410, (2016).
• [66] Hazim, J. M., Hassan, H. A., Yassin, A. I. M., Tahir, N. M., Zabidi, A., Rizman, Z. I., ... and Wah, N. A., “Early detection of diabetic retinopathy by using deep learning neural network”, International Journal of Engineering & Technology, 7(411): 1997-2004, (2018)
• [67] Math, L., and Fatima, R., “Adaptive machine learning classification for diabetic retinopathy”, Multimedia Tools and Applications, 80(4): 5173-5186, (2020).
• [68] Zeng, X., Chen, H., Luo, Y., and Ye, W., “Automated diabetic retinopathy detection based on binocular siamese-like convolutional neural network”, IEEE Access, 7: 30744–30753, (2019).
• [69] Qomariah, D. U. N., Tjandrasa, H., and Fatichah, C., “Classification of diabetic retinopathy and normal retinal images using CNN and SVM”, 2019 12th International Conference on Information & Communication Technology and System (ICTS), 152-157, (2019).
• [70] Islam, K. T., Wijewickrema, S., and O'Leary, S. “Identifying diabetic retinopathy from OCT images using deep transfer learning with artificial neural networks”, IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), 281–286, (2019).
• [71] Fawcett, T., “An introduction to ROC analysis”, Pattern Recognit. Lett., 27(8): 861–874, (2006).
• [72] Mateen, M., Wen, J., Hassan, M., Nasrullah, N., Sun, S., and Hayat, S., “Automatic detection of diabetic retinopathy: A review on datasets, methods and evaluation metrics”, IEEE Access, 8: 48784–48811, (2020).