Comparative analysis of deep learning classifiers for diabetic retinopathy identification and detection

References

• Alyoubi WL, Shalash WM, Abulkhair MF. Diabetic retinopathy detection through deep learning techniques: a review. Inf Med Unlocked. 2020;20:100377.
   Google Scholar
• Ishtiaq U, Abdul Kareem S, Abdullah ER, et al. Diabetic retinopathy detection through artificial intelligent techniques: a review and open issues. Multimed Tools Appl. 2020;79(21):15209–15252.
   Google Scholar
• Rajesh P, Shajin FH, Kommula BN. An efficient integration and control approach to increase the conversion efficiency of high-current low-voltage DC/DC converter. Energy Syst. 2022;13(4):939–958.
   Google Scholar
• Gadekallu TR, Khare N, Bhattacharya S, et al. Deep neural networks to predict diabetic retinopathy. J Ambient Intell Humaniz Comput. 2020:1–4.
   Google Scholar
• Rajesh P, Shajin FH. Optimal allocation of EV charging spots and capacitors in distribution network improving voltage and power loss by quantum-behaved and Gaussian mutational dragonfly algorithm (QGDA). Electr Power Syst Res . 2021;194:107049.
   Web of Science ®Google Scholar
• Shajin FH, Rajesh P, Thilaha S. Bald eagle search optimization algorithm for cluster head selection with prolong lifetime in wireless sensor network. J Soft Comput Eng Appl. 2020;1(1):7.
   Google Scholar
• Araújo T, Aresta G, Mendonça L, et al. DR| GRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images. Med Image Anal. 2020;63:101715.
   PubMedGoogle Scholar
• Shajin FH, Rajesh P, Raja MR. An efficient VLSI architecture for fast motion estimation exploiting zero motion prejudgment technique and a new quadrant-based search algorithm in HEVC. Circ Syst Signal Process. 2022;41(3):1751–1774.
   Web of Science ®Google Scholar
• Shankar K, Sait AR, Gupta D, et al. Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model. Pattern Recognit Lett. 2020;133:210–216.
   Web of Science ®Google Scholar
• Dayana AM, Emmanuel WR. An enhanced swarm optimization-based deep neural network for diabetic retinopathy classification in fundus images. Multimed Tools Appl. 2022;81(15):20611–20642.
   Google Scholar
• Alyoubi WL, Abulkhair MF, Shalash WM. Diabetic retinopathy fundus image classification and lesions localization system using deep learning. Sensors. 2021;21(11):3704.
   PubMedGoogle Scholar
• Bellemo V, Lim G, Rim TH, et al. Artificial intelligence screening for diabetic retinopathy: the real-world emerging application. Curr Diab Rep. 2019;19(9):1–2.
   PubMedGoogle Scholar
• Maqsood S, Damaševičius R, Maskeliūnas R. Hemorrhage detection based on 3D CNN deep learning framework and feature fusion for evaluating retinal abnormality in diabetic patients. Sensors. 2021;21(11):3865.
   PubMedGoogle Scholar
• Gayathri S, Gopi VP, Palanisamy P. A lightweight CNN for diabetic retinopathy classification from fundus images. Biomed Signal Process Control. 2020;62:102115.
   Google Scholar
• Maqsood S, Damaševičius R, Shah FM, et al. Detection of macula and recognition of aged-related macular degeneration in retinal fundus images. Comput Inf. 2021;40(5):957–987.
   Google Scholar
• Kattenborn T, Leitloff J, Schiefer F, et al. Review on convolutional neural networks (CNN) in vegetation remote sensing. ISPRS J Photogramm Remote Sens. 2021;173:24–49.
   Web of Science ®Google Scholar
• Khan MA. HCRNNIDS: hybrid convolutional recurrent neural network-based network intrusion detection system. Processes. 2021;9(5):834.
   Google Scholar
• Nawaz M, Mehmood Z, Nazir T, et al. Skin cancer detection from dermoscopic images using deep learning and fuzzy k-means clustering. Microsc Res Tech. 2022;85(1):339–351.
   PubMed Web of Science ®Google Scholar
• Guo Y, Zhang Z, Tang F. Feature selection with kernelized multi-class support vector machine. Pattern Recognit. 2021;117:107988.
   Google Scholar
• Zeng J, Guo Y, Han Y, et al. A review of the discriminant analysis methods for food quality based on near-infrared spectroscopy and pattern recognition. Molecules. 2021;26(3):749.
   PubMedGoogle Scholar
• Bhardwaj C, Jain S, Sood M. Hierarchical severity grade classification of non-proliferative diabetic retinopathy. J Ambient Intell Humaniz Comput. 2021;12(2):2649–2670.
   Google Scholar
• Ramasamy LK, Padinjappurathu SG, Kadry S, et al. Detection of diabetic retinopathy using a fusion of textural and ridgelet features of retinal images and sequential minimal optimization classifier. PeerJ Comput Sci. 2021;7:e456.
   PubMedGoogle Scholar
• Singh LK, Garg H. Automated glaucoma type identification using machine learning or deep learning techniques. In: Verma O, Roy S, Pandey S, Mittal M, editors. Advancement of machine intelligence in interactive medical image analysis. Singapore: Springer; 2020. p. 241–263.
   Google Scholar
• Barros D, Moura JC, Freire CR, et al. Machine learning applied to retinal image processing for glaucoma detection: review and perspective. Biomed Eng Online. 2020;19(1):1–21.
   PubMedGoogle Scholar
• Kaur P, Khosla PK. Artificial intelligence based glaucoma detection. In: Verma O, Roy S, Pandey S, Mittal M, editors. Advancement of machine intelligence in interactive medical image analysis. Singapore: Springer; 2020. p. 283–305.
   Google Scholar
• Hemanth DJ, Deperlioglu O, Kose U. An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network. Neural Comput Appl. 2020;32(3):707–721.
   Web of Science ®Google Scholar
• Math L, Fatima R. Adaptive machine learning classification for diabetic retinopathy. Multimed Tools Appl. 2021;80(4):5173–5186.
   Google Scholar
• Adriman R, Muchtar K, Maulina N. Performance evaluation of binary classification of diabetic retinopathy through deep learning techniques using texture feature. Procedia Comput Sci. 2021;179:88–94.
   Google Scholar
• Deng M, Meng T, Cao J, et al. Heart sound classification based on improved MFCC features and convolutional recurrent neural networks. Neural Netw. 2020;130:22–32.
   PubMedGoogle Scholar
• Pisner DA, Schnyer DM. Support vector machine. In: Machine learning. Academic Press; 2020. p. 101–121.
   Google Scholar
• Pitchai R, Supraja P, Victoria AH, et al. Brain tumor segmentation using deep learning and fuzzy k-means clustering for magnetic resonance images. Neural Process Lett. 2021;53(4):2519–2532.
   Web of Science ®Google Scholar
• Franco P, Coronado-Gutierrez D, Lopez C, et al. Glaucoma patient screening from online retinal fundus images via artificial intelligence. medRxiv. 2021.
   PubMedGoogle Scholar
• Hatamizadeh A, Hosseini H, Patel N, et al. RAVIR: a dataset and methodology for the semantic segmentation and quantitative analysis of retinal arteries and veins in infrared reflectance imaging. IEEE J Biomed Health Inform. 2022;26(7):3272–3283.
   PubMedGoogle Scholar
• Orujov F, Maskeliūnas R, Damaševičius R, et al. Fuzzy based image edge detection algorithm for blood vessel detection in retinal images. Appl Soft Comput. 2020;94:106452.
   Web of Science ®Google Scholar
• Wu Y, Xia Y, Song Y, et al. NFN+: a novel network followed network for retinal vessel segmentation. Neural Netw. 2020;126:153–162.
   PubMedGoogle Scholar