Accès libre

Advanced Diabetic Retinopathy Detection with the R–CNN: A Unified Visual Health Solution

, , ,  et   
25 déc. 2024
À propos de cet article

Citez
Télécharger la couverture

Albahli, S., Nazir, T., Irtaza, A. and Javed, A. (2021). Recognition and detection of diabetic retinopathy using Densenet-65 based Faster-R-CNN, Computers, Materials & Continua 67(2): 1333–1351.Search in Google Scholar

Alwakid, G., Gouda, W. and Humayun, M. (2023). Deep learning-based prediction of diabetic retinopathy using CLAHE and ESRGAN for enhancement, Healthcare 11(6).Search in Google Scholar

Alyoubi, W.L., Shalash, W.M. and Abulkhair, M.F. (2020). Diabetic retinopathy detection through deep learning techniques: A review, Informatics in Medicine Unlocked 20(2020): 100377.Search in Google Scholar

Aziza, E.Z., Mohamed El Amine, L., Mohamed, M. and Abdelhafid, B. (2019). Decision tree CART algorithm for diabetic retinopathy classification, 2019 6th International Conference on Image and Signal Processing and their Applications (ISPA), Mostaganem, Algeria, pp. 1–5.Search in Google Scholar

Barburiceanu, S., Terebes, R. and Meza, S. (2021). 3D texture feature extraction and classification using GLCM and LBP-based descriptors, Applied Sciences 11(5): 2332.Search in Google Scholar

Bergers, G. and Song, S. (2005). The role of pericytes in blood-vessel formation and maintenance, Neuro-Oncology 7(4): 452–464.Search in Google Scholar

Bhandari, S., Pathak, S. and Jain, S. (2022). A literature review of early-stage diabetic retinopathy detection using deep learning and evolutionary computing techniques, Archives of Computational Methods in Engineering 30(2): 799–810.Search in Google Scholar

Carrera, E. V., González, A. and Carrera, R. (2017). Automated detection of diabetic retinopathy using SVM, 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), Cusco, Peru, pp. 1–4.Search in Google Scholar

Das, D., Biswas, S. and Bandyopadhyay, S. (2022). Detection of diabetic retinopathy using convolutional neural networks for feature extraction and classification (DRFEC), Multimedia Tools and Applications 82(19): 1–59.Search in Google Scholar

Fayyaz, A.M., Sharif, M.I., Azam, S., Karim, A. and El-Den, J. (2023). Analysis of diabetic retinopathy (DR) based on the deep learning, Information 14(1): 30.Search in Google Scholar

Guleria, P., Naga Srinivasu, P. and Hassaballah, M. (2023). Diabetes prediction using Shapley additive explanations and DSaaS over machine learning classifiers: A novel healthcare paradigm, Multimedia Tools and Applications 83: 40677–40712.Search in Google Scholar

Gupta, A. and Chhikara, R. (2018). Diabetic retinopathy: Present and past, Procedia Computer Science 132(2018): 1432–1440.Search in Google Scholar

Harun, N.H., Yusof, Y., Hassan, F. and Embong, Z. (2019). Classification of fundus images for diabetic retinopathy using artificial neural network, 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), Amman, Jordan, pp. 498–501.Search in Google Scholar

Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R.R. (2012). Improving neural networks by preventing co-adaptation of feature detectors arXiv: 1207.0580.Search in Google Scholar

IDF (2024). IDF Diabetes Atlas, 8th Edition, International Diabetes Federation, Brussels, https://diabetesatlas.org/.Search in Google Scholar

Kandan, R. and Aruna, P. (2012). SVM and neural network based diagnosis of diabetic retinopathy, International Journal of Computer Applications 41(1): 6–12.Search in Google Scholar

Kandel, I. and Castelli, M. (2020). Transfer learning with convolutional neural networks for diabetic retinopathy image classification. A review, Applied Sciences 10(6): 1–24.Search in Google Scholar

Kaur, J. and Kaur, P. (2022). Automated computer-aided diagnosis of diabetic retinopathy based on segmentation and classification using K-nearest neighbor algorithm in retinal images, The Computer Journal 66(8): 2011–2032.Search in Google Scholar

Li, X., Hu, X., Yu, L., Zhu, L., Fu, C.-W. and Heng, P.-A. (2020). CANet: Cross-disease attention network for joint diabetic retinopathy and diabetic macular edema grading, IEEE Transactions on Medical Imaging 39(5): 1483–1493.Search in Google Scholar

Liu, C., Ge, H.-M., Liu, B.-H., Dong, R., Shan, K., Chen, X., Yao, M.-D., Li, X.-M., Yao, J., Zhou, R.-M., Zhang, S.-J., Zhao, C. and Yan, B. (2019). Targeting pericyte–endothelial cell crosstalk by circular RNA-cPWWP2A inhibition aggravates diabetes-induced microvascular dysfunction, Proceedings of the National Academy of Sciences 116: 7455–7464.Search in Google Scholar

Mohanty, C., Mahapatra, S., Acharya, B., Kokkoras, F., Gerogiannis, V.C., Karamitsos, I. and Kanavos, A. (2023). Using deep learning architectures for detection and classification of diabetic retinopathy, Sensors 23(12): 1–18.Search in Google Scholar

Naga Srinivasu, P., Ijaz, M.F. and Woźniak, M. (2024). XAI-driven model for crop recommender system for use in precision agriculture, Computational Intelligence 40(1): e12629.Search in Google Scholar

Nur-A-Alam, M., Nasir, M.M.K., Ahsan, M., Based, M.A., Haider, J. and Palani, S. (2023). A faster R-CNN-based diabetic retinopathy detection method using fused features from retina images, IEEE Access 11: 124331–124349.Search in Google Scholar

Oh, K., Kang, H., Leem, D., Lee, H., Seo, K.Y. and Yoon, S. (2021). Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images, Scientific Reports 11: 1–9.Search in Google Scholar

Patel, S.N., Shi, A., Wibbelsman, T.D. and Klufas, M.A. (2020). Ultra-widefield retinal imaging: An update on recent advances, Therapeutic Advances in Ophthalmology 12: 2515841419899495.Search in Google Scholar

Priyadharsini, C. and Jagadeesh Kannan, R. (2023). Retinal image enhancement based on color dominance of image, Scientific Reports 13: 7172.Search in Google Scholar

Rajesh, A.E., Davidson, O.Q., Lee, C.S. and Lee, A.Y. (2023). Artificial intelligence and diabetic retinopathy: AI framework, prospective studies, head-to-head validation, and cost-effectiveness, Diabetes Care 46(10): 1728–1739.Search in Google Scholar

Rakshitha, T.R., Devaraj, D. and Kumar, S.C.P. (2016). Comparative study of imaging transforms on diabetic retinopathy images, 2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT), Bangalore, India pp. 118–122.Search in Google Scholar

Ramanathan, R.S. (2017). Correlation of duration, hypertension and glycemic control with microvascular complications of diabetes mellitus at a tertiary care hospital, Integrative Molecular Medicine 4(4): 1–4.Search in Google Scholar

Ren, S., He, K., Girshick, R. and Sun, J. (2017). Faster r-CNN: Towards real-time object detection with region proposal networks, IEEE Transactions on Pattern Analysis and Machine Intelligence 39(6): 1137–1149.Search in Google Scholar

Romeo, G., Liu, W.-H., Asnaghi, V., Kern, T.S. and Lorenzi, M. (2002). Activation of nuclear factor kB induced by diabetes and high glucose regulates a proapoptotic program in retinal pericytes, Diabetes 51(7): 2241–2248.Search in Google Scholar

Romero-Aroca, P., Baget-Bernaldiz, M., Pareja-Rios, A., Lopez-Galvez, M., Navarro-Gil, R. and Verges, R. (2016). Diabetic macular edema pathophysiology: Vasogenic versus inflammatory, Journal of Diabetes Research 2016(1): 1–17.Search in Google Scholar

Sakthi Sree Devi, M., Ramkumar, S., Vinuraj Kumar, S. and Sasi, G. (2021). Detection of diabetic retinopathy using OCT image, Materials Today: Proceedings 47: 185–190.Search in Google Scholar

Sebastian, A., Elharrouss, O., Al-Maadeed, S. and Almaadeed, N. (2023). A survey on deep-learning-based diabetic retinopathy classification, Diagnostics 13(3): 1–22.Search in Google Scholar

Solomon, S.D., Chew, E., Duh, E.J., Sobrin, L., Sun, J.K., VanderBeek, B.L., Wykoff, C.C. and Gardner, T.W. (2017). Diabetic retinopathy: A position statement by the American Diabetes Association, Diabetes Care 40(3): 412–418.Search in Google Scholar

Sorrentino, F.S., Allkabes, M., Salsini, G., Bonifazzi, C. and Perri, P. (2016). The importance of glial cells in the homeostasis of the retinal microenvironment and their pivotal role in the course of diabetic retinopathy, Life Sciences 162: 54–59.Search in Google Scholar

Tomita, Y., Lee, D., Tsubota, K., Negishi, K. and Kurihara, T. (2021). Updates on the current treatments for diabetic retinopathy and possibility of future oral therapy, Journal of Clinical Medicine 10(20): 1–19.Search in Google Scholar

Sudha, V. and Ganeshbabu, T.R. (2021). A convolutional neural network classifier VGG-19 architecture for lesion detection and grading in diabetic retinopathy based on deep learning, Computers, Materials & Continua 66(1): 827–842.Search in Google Scholar

Vankadaru, V., Penugonda, G., Parvathaneni, N. and Bhoi, A.K. (2023). Heart failure prediction using radial basis with metaheuristic optimization, in P. Barsocchi et al. (Eds), Enabling Person-Centric Healthcare Using Ambient Assistive Technology, Springer, Cham, pp. 129–150.Search in Google Scholar

Vinayaki, V. and Kalaiselvi, R. (2022). Multithreshold image segmentation technique using remora optimization algorithm for diabetic retinopathy detection from fundus images, Neural Processing Letters 54: 2363–2384.Search in Google Scholar

Wang, W. and Lo, A.C.Y. (2018). Diabetic retinopathy: Pathophysiology and treatments, International Journal of Molecular Sciences 19(6): 1–14.Search in Google Scholar

Yasashvini, R., Raja Sarobin, V., Panjanathan, R.,J., Yasmine, G. and Anbarasi, J. (2022). Diabetic retinopathy classification using CNN and hybrid deep convolutional neural networks, Symmetry 14(9): 1–13.Search in Google Scholar

Langue:
Anglais
Périodicité:
4 fois par an
Sujets de la revue:
Mathématiques, Mathématiques appliquées