Deep learning based binary classification of diabetic retinopathy images using transfer learning approach

Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016;316(22):2402–10. https://doi.org/10.1001/jama.2016.17216.

Article  PubMed  Google Scholar 

Chandrakumar T, Kathirvel R. Classifying diabetic retinopathy using deep learning architecture. Int J Eng Res. 2016;5:19–24.

Google Scholar 

Zhou L, Zhao Y, Yang J, Yu Q, Xu X. Deep multiple instance learning for automatic detection of diabetic retinopathy in retinal images. IET Image Proc. 2018;12(4):563–71. https://doi.org/10.1049/iet-ipr.2017.0636.

Article  Google Scholar 

Dutta S, Manideep BCS, Basha SM, Caytiles RD, Iyengar NCSN. Classification of diabetic retinopathy images by using deep learning models. Int J Grid Distrib Comput. 2018;11(1):89–106. https://doi.org/10.14257/ijgdc.2018.11.1.09.

Article  Google Scholar 

Junjun P, Zhifan Y, Dong S, Hong, Q. Diabetic Retinopathy Detection Based on Deep Convolutional Neural Networks for Localization of Discriminative Regions. Proceedings - 8th International Conference on Virtual Reality and Visualization, ICVRV 2018;46–52. https://doi.org/10.1109/ICVRV.2018.00016

Kassani SH, Kassani PH, Khazaeinezhad R, Wesolowski MJ, Schneider KA, Deters R. Diabetic retinopathy classification using a modified xception architecture. 2019 IEEE 19th International Symposium on Signal Processing and Information Technology, ISSPIT. 2019. https://doi.org/10.1109/ISSPIT47144.2019.9001846

Challa UK, Yellamraju P, Bhatt JS. A Multi-class Deep All-CNN for detection of diabetic retinopathy using retinal fundus images. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11941 LNCS. 2019;191–199. https://doi.org/10.1007/978-3-030-34869-4_21

Qummar S, Khan FG, Shah S, Khan A, Shamshirband S, Rehman ZU, Khan IA, Jadoon W. A Deep Learning ensemble approach for diabetic retinopathy detection. IEEE Access. 2019;7:150530–9. https://doi.org/10.1109/ACCESS.2019.2947484.

Article  Google Scholar 

Bhardwaj C, Jain S, Sood M. Diabetic retinopathy severity grading employing quadrant-based Inception-V3 convolution neural network architecture. Int J Imaging Syst Technol. 2021;31(2):592–608. https://doi.org/10.1002/ima.22510.

Article  Google Scholar 

Saxena G, Verma DK, Paraye A, Rajan A, Rawat A. Improved and robust deep learning agent for preliminary detection of diabetic retinopathy using public datasets. Intelligence-Based Med. 2020;3–4. https://doi.org/10.1016/j.ibmed.2020.100022

Katada Y, Ozawa N, Masayoshi K, Ofuji Y, Tsubota K, Kurihara T. Automatic screening for diabetic retinopathy in interracial fundus images using artificial intelligence. Intelligence-Based Med. 2020;3–4. https://doi.org/10.1016/j.ibmed.2020.100024

Usman, A., Muhammad, A., Martinez-Enriquez, A. M., & Muhammad, A. (2020). Classification of Diabetic Retinopathy and Retinal Vein Occlusion in Human Eye Fundus Images by Transfer Learning. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), Advances in Information and Communication (pp. 642–653). FICC 2020. Adv Intell Syst Comput.2020;1130. Springer, Cham. https://doi.org/10.1007/978-3-030-39442-4_47.

Alyoubi WL, Abulkhair MF, Shalash WM. Diabetic retinopathy fundus image classification and lesions localization system using deep learning. Sensors. 2021;21(11). https://doi.org/10.3390/s21113704

Bhardwaj C, Jain S, Sood M. Deep learning-based diabetic retinopathy severity grading system employing quadrant ensemble model. J Digit Imaging. 2021;34(2):440–57. https://doi.org/10.1007/s10278-021-00418-5.

Article  PubMed  PubMed Central  Google Scholar 

Chen PN, Lee CC, Liang CM, Pao SI, Huang KH, Lin KF. General deep learning model for detecting diabetic retinopathy. BMC Bioinformatics. 2021;22. https://doi.org/10.1186/s12859-021-04005-x

Yi SL, Yang XL, Wang TW, She FR, Xiong X, He JF. Diabetic retinopathy diagnosis based on RA-efficientnet. Applied Sciences (Switzerland). 2021;11(22):11035. https://doi.org/10.3390/app112211035.

Article  CAS  Google Scholar 

Khan Z, Khan FG, Khan A, Rehman ZU, Shah S, Qummar S, Ali F, Pack S. Diabetic retinopathy detection using vgg-nin a deep learning architecture. IEEE Access. 2021;9:61408–16. https://doi.org/10.1109/ACCESS.2021.3074422.

Article  Google Scholar 

Das S, Kharbanda K, M S, Raman R, DED. Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy. Biomed Signal Process Control. 2021;68, 102600. https://doi.org/10.1016/j.bspc.2021.102600.

AbdelMaksoud E, Barakat S, Elmogy M. A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique. Med Biol Eng Compu. 2022;60(7):2015–38. https://doi.org/10.1007/s11517-022-02564-6.

Article  Google Scholar 

Kobat SG, Baygin N, Yusufoglu E, Baygin M, Barua PD, Dogan S, Yaman O, Celiker U, Yildirim H, Tan RS, Tuncer T, Islam N, Acharya UR. Automated diabetic retinopathy detection using horizontal and vertical patch division-Based Pre-Trained DenseNET with digital fundus images. Diagnostics. 2022;12(8):1975. https://doi.org/10.3390/diagnostics12081975.

Article  PubMed  PubMed Central  Google Scholar 

Mungloo-Dilmohamud Z, Khan MHM, Jhumka K, Beedassy BN, Mungloo NZ, Peña-Reyes C. Balancing data through data augmentation improves the generality of transfer learning for diabetic retinopathy classification. Appl Sci (Switzerland). 2022;12(11):5363. https://doi.org/10.3390/app12115363.

Article  CAS  Google Scholar 

Asia AO, Zhu CZ, Althubiti SA, Al-Alimi D, Xiao YL, Ouyang PB, Al-Qaness MAA. Detection of diabetic retinopathy in retinal fundus images using CNN classification models. Electronics (Switzerland). 2022;11(17):2740. https://doi.org/10.3390/electronics11172740.

Article  Google Scholar 

Mondal SS, Mandal N, Singh KK, Singh A, Izonin I. EDLDR: An ensemble deep learning technique for detection and classification of diabetic retinopathy. Diagnostics. 2023;13(1):124. https://doi.org/10.3390/diagnostics13010124.

Article  Google Scholar 

Yasashvini R, Raja Sarobin VM, Panjanathan R, Graceline S, Anbarasi JL. Diabetic retinopathy classification using CNN and hybrid deep convolutional neural networks. Symmetry. 2022;14(9):1932. https://doi.org/10.3390/sym14091932.

Article  Google Scholar 

Dayana AM, Emmanuel WRS. Deep learning enabled optimized feature selection and classification for grading diabetic retinopathy severity in the fundus image. Neural Comput Appl. 2022;34(21):18663–83. https://doi.org/10.1007/s00521-022-07471-3.

Article  Google Scholar 

Oulhadj M, Riffi J, Chaimae K, Mahraz AM, Ahmed B, Yahyaouy A, Fouad C, Meriem A, Idriss BA, Tairi H. Diabetic retinopathy prediction based on deep learning and deformable registration. Multimedia Tools and Applications. 2022;81(20):28709–27. https://doi.org/10.1007/s11042-022-12968-z.

Article  Google Scholar 

Jabbar MK, Yan J, Xu H, Rehman ZU, Jabbar A. Transfer learning-based model for diabetic retinopathy diagnosis using retinal images. Brain Sci. 2022;12(5):535. https://doi.org/10.3390/brainsci12050535.

Article  PubMed  PubMed Central  Google Scholar 

Menaouer B, Dermane Z, el Houda Kebir N, Matta N. diabetic retinopathy classification using hybrid deep learning approach. SN Comp Sci. 2022;3(5). https://doi.org/10.1007/s42979-022-01240-8

Fayyaz AM, Sharif MI, Azam S, Karim A, El-Den J. Analysis of diabetic retinopathy (DR) based on the deep learning. Information (Switzerland). 2023;14(1):30. https://doi.org/10.3390/info14010030.

Article  Google Scholar 

Das D, Biswas SK, Bandyopadhyay S. Detection of diabetic retinopathy using convolutional neural networks for feature extraction and classification (DRFEC). Multimedia Tools Appl. 2023;82(19):29943–30001. https://doi.org/10.1007/s11042-022-14165-4.

Article  Google Scholar 

Mohanty C, Mahapatra S, Acharya B, Kokkoras F, Gerogiannis VC, Karamitsos I, Kanavos A. Using deep learning architectures for detection and classification of diabetic retinopathy. Sensors. 2023;23(12):5726. https://doi.org/10.3390/s23125726.

Article  PubMed  PubMed Central  Google Scholar 

Jena PK, Khuntia B, Palai C, Nayak M, Mishra TK, Mohanty SN. A novel approach for diabetic retinopathy screening using asymmetric deep learning features. Big Data Cogn Comput. 2023;7(1):25. https://doi.org/10.3390/bdcc7010025.

Article  Google Scholar 

Bhimavarapu U, Chintalapudi N, Battineni G. automatic detection and classification of diabetic retinopathy using the improved pooling function in the convolution neural network. Diagnostics. 2023;13(15):2606. https://doi.org/10.3390/diagnostics13152606.

Article  PubMed  PubMed Central  Google Scholar 

Islam N, Jony MdMH, Hasan E, Sutradhar S, Rahman A, Islam MdM. Toward lightweight diabetic retinopathy classification: A knowledge distillation approach for resource-constrained settings. Appl Sci. 2023;13(22):12397. https://doi.org/10.3390/app132212397.

Article  CAS  Google Scholar 

Sajid MZ, Hamid MF, Youssef A, Yasmin J, Perumal G, Qureshi I, Naqi SM, Abbas Q. DR-NASNet: automated system to detect and classify diabetic retinopathy severity using improved pretrained NASNet model. Diagnostics. 2023;13(16):2645. https://doi.org/10.3390/diagnostics13162645.

Article  PubMed  PubMed Central  Google Scholar 

Alwakid G, Gouda W, Humayun M. Enhancement of diabetic retinopathy prognostication using deep learning, CLAHE, and ESRGAN. Diagnostics. 2023. https://doi.org/10.3390/diagnostics.

Article  PubMed  PubMed Central  Google Scholar 

Vijayan M, Venkatakrishnan S. A regression-based approach to diabetic retinopathy diagnosis using efficientnet. Diagnostics. 2023;13(4):774. https://doi.org/10.3390/diagnostics13040774.

Article  PubMed  PubMed Central  Google Scholar 

Alwakid G, Gouda W, Humayun M, Jhanjhi NZ. Deep learning-enhanced diabetic retinopathy image classification. Digital Health. 2023;9. https://doi.org/10.1177/20552076231194942

Guefrachi S, Echtioui A, Hamam H. Automated diabetic retinopathy screening using deep learning. Multimedia Tools Appl. 2024. https://doi.org/10.1007/s11042-024-18149-4.

Article  Google Scholar 

Sunkari S, Sangam A, P VS, Manikandan S, Raman R, Rajalakshmi R, S T. A refined ResNet18 architecture with Swish activation function for Diabetic Retinopathy classification. Biomedical Signal Processing and Control. 2024;88, 105630. https://doi.org/10.1016/j.bspc.2023.105630.

Macsik P, Pavlovicova J, Kajan S, Goga J, Kurilova V. Image preprocessing-based ensemble deep learning classification of diabetic retinopathy. IET Image Proc. 2024;18(3):807–28. https://doi.org/10.1049/ipr2.12987.

Article  Google Scholar 

留言 (0)

沒有登入
gif