Predicting diabetic retinopathy stage using Siamese Convolutional Neural Network

References

• Baarslag T, Hendrikx MJ, Hindriks KV, Jonker CM 2016. A survey of opponent modeling techniques in automated negotiation. In: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems; Richland, SC. International Foundation for Autonomous Agents and Multiagent Systems. p. 575–13. AAMAS ’16.
   Google Scholar
• Balestriero R, Bottou L, LeCun Y. 2022. The effects of regularization and data augmentation are class dependent. Adv Neural Inf Process Syst. 35:37878–37891.
   Google Scholar
• Bengio ACPV Y. 2018. Representation learning: a review and new perspectives. IEEE T Pattern Anal. 35(8):178–1828. doi: 10.1109/TPAMI.2013.50.
   Google Scholar
• Bilal A, Zhu L, Deng A, Lu H, Wu N. 2022. Ai-based automatic detection and classification of diabetic retinopathy using u-net and deep learning. Symmetry. 14(7):1427. doi:10.3390/sym14071427.
   Google Scholar
• Brownlee J. 2019. Use early stopping to halt the training of neural networks at the right time. [accessed 2023 Oct 09]. https://machinelearningmastery.com/how-to-stop-training-deep-neural-networks-at-the-right-time-using-early-stopping/.
   Google Scholar
• Chopra S, Hadsell R, LeCun Y 2005. Learning a similarity metric discriminatively, with application to face verification. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) - Volume 1 - Volume 01; Washington, DC, USA. IEEE Computer Society. p. 539–546. CVPR ’05: 10.1109/CVPR.2005.202.
   Google Scholar
• Chung Y, Weng W. 2017. Learning deep representations of medical images using siamese cnns with application to content-based image retrieval. CoRr abs/171108490. http://arxiv.org/abs/1711.08490.
   Google Scholar
• Decencière E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, Gain P, Ordonez R, Massin P, Erginay A, et al. 2014. Feedback on a publicly distributed database: the messidor database. Image Anal Stereol. 33(3):231–234. http://www.ias-iss.org/ojs/IAS/article/view/1155
   Google Scholar
• Deepa V, Sathish Kumar C, Cherian T. 2022. Automated grading of diabetic retinopathy using cnn with hierarchical clustering of image patches by siamese network. Phy & Eng Sci In Med. 45(2):623–635. doi:10.1007/s13246-022-01129-z.
   PubMedGoogle Scholar
• El Hossi A, Skouta A, Elmoufidi A, Nachaoui M. 2021. Applied cnn for automatic diabetic retinopathy assessment using fundus images. In: Fakir M, Baslam M, and El Ayachi R, editors Business intelligence. Cham: Springer International Publishing; pp. 425–433.
   Google Scholar
• Elmoufidi A, Ammoun H. 2022. Diabetic retinopathy prevention using efficientnetb3 architecture and fundus photography. Sn Comput Sci. 4(1). doi: 10.1007/s42979-022-01482-6.
   Google Scholar
• Fasel B. 2002. Robust face analysis using convolutional neural networks. In: 2002 International Conference on Pattern Recognition; Quebec City, QC, Canada. vol. 2; p. 40–43.
   Google Scholar
• Fayyaz AM, Sharif MI, Azam S, Karim A, El-Den J. 2023. Analysis of diabetic retinopathy (dr) based on the deep learning. Information. 14(1):30. doi:10.3390/info14010030.
   Google Scholar
• Gayathri S, Gopi V, Palanisamy P. Diabetic retinopathy classification based on multipath cnn and machine learning classifiers. Phys Eng Sci Med. 2021. Epub 2021 May 2544 3: 639–653. 10.1007/s13246-021-01012-3.
   PubMedGoogle Scholar
• Hadsell R, Chopra S, LeCun Y 2006. Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06); New York, NY, USA. vol. 2. p. 1735–1742.
   Google Scholar
• Jena PK, Khuntia B, Palai C, Nayak M, Mishra TK, Mohanty SN. 2023. A novel approach for diabetic retinopathy screening using asymmetric deep learning features. Big Data And Cognit Comput. 7(1):25. https://www.mdpi.com/2504-2289/7/1/25.
   Google Scholar
• Jiwani N, Gupta K, Sharif MHU, Datta R, Habib F, Afreen N. 2023. Application of transfer learning approach for diabetic retinopathy classification. Proceedings of the 2023 International Conference on Power Electronics and Energy (ICPEE); KIIT School of Electrical Engineering, Bhubaneswar, India. p. 1–4.
   Google Scholar
• Karthik, M. 2019. Aptos 2019 Blindness Detection. https://kaggle.com/competitions/aptos2019-blindness-detection.
   Google Scholar
• Kauppi T, Kalesnykiene V, Kamarainen Joni-K, Lensu L, Sorri I, Uusitalo H, Kälviäinen H, Pietilä J. 2006. DIARETDB0: Evaluation database and methodology for diabetic retinopathy algorithms. Machine Vision and Pattern Recognition Research Group, Lappeenranta University of Technology, Finland. Vol. 73. p. 1–17. https://api.semanticscholar.org/CorpusID:573081
   Google Scholar
• Khalajzadeh H, Manthouri M, Teshnehlab M. 2013. Hierarchical structure based convolutional neural network for face recognition. Int J Comp Intel Appl. 12(3):1350018. doi: 10.1142/S1469026813500181.
   Google Scholar
• Koch G, Zemel R, Salakhutdinov R. 2015. Siamese neural networks for one-shot image recognition. In: ICML deep learning workshop; vol. 2. Lille. p. 1–8.
   Google Scholar
• Lam C, Yi D, Guo M, Lindsey T. 2018. Automated detection of diabetic retinopathy using deep learning. Proceedings of the AMIA summits on translational science; San Francisco, CA, USA. American Medical Informatics Association. p. 147–155.
   Google Scholar
• Lecun Y, Bottou L, Bengio Y, Haffner P 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE; Lille, France. p. 2278–2324.
   Google Scholar
• LeCun Y, Huang FJ 2005. Loss functions for discriminative training of energy-based models. In: Cowell R Ghahramani Z, editors Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics; (Proceedings of Machine Learning Research; vol. R5); 06–08 Jan. PMLR. p. 206–213. Reissued by PMLR on 30 March 2021: https://proceedings.mlr.press/r5/lecun05a.html.
   Google Scholar
• Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JA, van Ginneken B, Sánchez CI. 2017. A survey on deep learning in medical image analysis. Med Image Anal. 42:60–88. https://www.sciencedirect.com/science/article/pii/S1361841517301135.
   PubMed Web of Science ®Google Scholar
• Mathers, CD, Loncar, D. 2006. Projections of Global Mortality and Burden of Disease from 2002 to 2030. PLOS Medicine. Vol. 3; pp. 1–20. doi:10.1371/journal.pmed.0030442.
   Google Scholar
• Matsugu M, Mori K, Mitari Y, Kaneda Y. 2003. Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Net. 16(5):555–559. Advances in Neural Networks Research: IJCNN ’03; http://www.sciencedirect.com/science/article/pii/S0893608003001151.
   PubMed Web of Science ®Google Scholar
• Mondal SS, Mandal N, Singh KK, Singh A, Izonin I. 2023. Edldr: an ensemble deep learning technique for detection and classification of diabetic retinopathy. Diagn. 13(1):124. https://www.mdpi.com/2075-4418/13/1/124.
   Google Scholar
• Mutawa AM, Alnajdi S, Sruthi S. 2023. Transfer learning for diabetic retinopathy detection: a study of dataset combination and model performance. Appl Sci. 13(9):5685. doi:10.3390/app13095685.
   Google Scholar
• Porwal P, Pachade S, Kamble R, Kokare M, Deshmukh G, Sahasrabuddhe V, Meriaudeau F. 2018. Indian diabetic retinopathy image dataset (idrid). doi: 10.21227/H25W98.
   Google Scholar
• Ramesh P, RameshData P, Ramesh P 2019. Face recognition using siamese networks [tutorial]. Feb: https://hub.packtpub.com/face-recognition-using-siamese-networks-tutorial/.
   Google Scholar
• RR Bourne ea. 2013. Causes of vision loss worldwide, 1990-2010: a systematic analysis. In: Projections of global mortality and burden of disease from 2002 to 2030;. Vol. 1, Lancet Glob Health; pp. 339–349.
   Google Scholar
• Skouta A, Elmoufidi A, Jai-Andaloussi S, Ouchetto O 2023. Deep learning for diabetic retinopathy assessments: a literature review. Multimedia Tools And Applications:1–66. https://api.semanticscholar.org/CorpusID:258093969.
   Web of Science ®Google Scholar
• Teh YW, Welling M, Osindero S, Hinton GE. 2003. Energy-based models for sparse overcomplete representations. J Mach Learn Res. 4:1235–1260. http://dl.acm.org/citation.cfm?id=945365.964304.
   Web of Science ®Google Scholar
• Voets M, Møllersen K, Bongo LA, Ortega-Martorell S. 2019. 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. doi:10.1371/journal.pone.0217541.
   Web of Science ®Google Scholar
• who. 2017. World health organization. [Accessed 10/1/2018]. http://www.who.int/news-room/fact-sheets/detail/diabetes.
   Google Scholar
• Wild Ea S. 2004. A survey of opponent modeling techniques in automated negotiation. Global Prevalence Of Diabetes: Estimates For The Year 2000 And Projections For 2030 Diabetes Care. 27(10):2569–2569. doi: 10.2337/diacare.27.10.2569-a.
   Google Scholar
• Zago GT, Andreão RV, Dorizzi B, Ottoni E. 2020. Diabetic retinopathy detection using red lesion localization and convolutional neural networks. Comput Biol Med. 116:103537. http://www.sciencedirect.com/science/article/pii/S0010482519303968.
   PubMed Web of Science ®Google Scholar