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Current Eye Research Volume 45, 2020 - Issue 12
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Retina/Choroid

Automatic Grading System for Diabetic Retinopathy Diagnosis Using Deep Learning Artificial Intelligence Software

Xiang-Ning Wanga Department of Ophthalmology, Shanghai Jiaotong University Affiliated Sixth People’s Hospital, Shanghai, ChinaORCID Iconhttps://orcid.org/0000-0003-2608-9026View further author information
ORCID Icon,
Ling Daib Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, ChinaView further author information
,
Shu-Ting Lia Department of Ophthalmology, Shanghai Jiaotong University Affiliated Sixth People’s Hospital, Shanghai, ChinaView further author information
,
Hong-Yu Konga Department of Ophthalmology, Shanghai Jiaotong University Affiliated Sixth People’s Hospital, Shanghai, ChinaView further author information
,
Bin Shengb Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, ChinaORCID Iconhttps://orcid.org/0000-0001-8510-2556View further author information
ORCID Icon &
Qiang Wua Department of Ophthalmology, Shanghai Jiaotong University Affiliated Sixth People’s Hospital, Shanghai, China;c Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, ChinaCorrespondence[email protected]
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Pages 1550-1555 | Received 25 Jun 2019, Accepted 22 Apr 2020, Published online: 15 May 2020

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M.Mary Vespa & C.Agees Kumar. (2024) Diabetic eye disease in computerised tomography of feature extraction and classification in hybrid neural network. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 12:1.
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Articles from other publishers (19)

Ruchika Bala, Arun Sharma & Nidhi Goel. (2023) Comparative Analysis of Diabetic Retinopathy Classification Approaches Using Machine Learning and Deep Learning Techniques. Archives of Computational Methods in Engineering 31:2, pages 919-955.
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S. Karthika & M. Durgadevi. (2024) Improved ResNet_101 assisted attentional global transformer network for automated detection and classification of diabetic retinopathy disease. Biomedical Signal Processing and Control 88, pages 105674.
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Rajasekhar Kommaraju & M.S. Anbarasi. (2024) Diabetic retinopathy detection using convolutional neural network with residual blocks. Biomedical Signal Processing and Control 87, pages 105494.
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Martina Tomić, Romano Vrabec, Đurđica Hendelja, Vilma Kolarić, Tomislav Bulum & Dario Rahelić. (2023) Diagnostic Accuracy of Hand-Held Fundus Camera and Artificial Intelligence in Diabetic Retinopathy Screening. Biomedicines 12:1, pages 34.
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Rajasekhar Kommaraju & M. S. Anbarasi. (2023) Detection of Diabetic Retinopathy (DR) Severity from Fundus Photographs using Swin Transformer. Detection of Diabetic Retinopathy (DR) Severity from Fundus Photographs using Swin Transformer.
A. S. Sabeena & M. K. Jeyakumar. (2023) GD-STFA: Gradient Descent Sea Turtle Foraging Algorithm enabled Deep Q Network for Diabetic Retinopathy Detection. Multimedia Tools and Applications 83:18, pages 53817-53836.
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Zhivko Zhelev, Jaime Peters, Morwenna Rogers, Michael Allen, Goda Kijauskaite, Farah Seedat, Elizabeth Wilkinson & Christopher Hyde. (2023) Test accuracy of artificial intelligence-based grading of fundus images in diabetic retinopathy screening: A systematic review. Journal of Medical Screening 30:3, pages 97-112.
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Zhijiang Wan, Jiachen Wan, Wangxinjun Cheng, Junqi Yu, Yiqun Yan, Hai Tan & Jianhua Wu. (2023) A Wireless Sensor System for Diabetic Retinopathy Grading Using MobileViT-Plus and ResNet-Based Hybrid Deep Learning Framework. Applied Sciences 13:11, pages 6569.
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Shtwai Alsubai, Abdullah Alqahtani, Adel Binbusayyis, Mohemmed Sha, Abdu Gumaei & Shuihua Wang. (2023) Quantum Computing Meets Deep Learning: A Promising Approach for Diabetic Retinopathy Classification. Mathematics 11:9, pages 2008.
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Jasmeen Randhawa, Michael Chiang, Natalia Porporato, Anmol A Pardeshi, Justin Dredge, Galo Apolo Aroca, Tin A Tun, Joanne HuiMin Quah, Marcus Tan, Risa Higashita, Tin Aung, Rohit Varma & Benjamin Y Xu. (2023) Generalisability and performance of an OCT-based deep learning classifier for community-based and hospital-based detection of gonioscopic angle closure. British Journal of Ophthalmology 107:4, pages 511-517.
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Ganeshsree Selvachandran, Shio Gai Quek, Raveendran Paramesran, Weiping Ding & Le Hoang Son. (2022) Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods. Artificial Intelligence Review 56:2, pages 915-964.
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Cristiano A. Kunas, Dayla R. Pinto, Lisandro Z. Granville, Matheus S. Serpa, Edson L. Padoin & Philippe O. A. Navaux. (2022) Edge Computing versus Cloud Computing: Impact on Retinal Image Pre-processing. Edge Computing versus Cloud Computing: Impact on Retinal Image Pre-processing.
Cristiano A. Künas, Dayla R. Pinto, Philippe O. A. Navaux & Lisandro Z. Granville. (2022) Computação de Borda versus Computação em Nuvem: Impacto do Pré-processamento de Imagens de Retinas. Computação de Borda versus Computação em Nuvem: Impacto do Pré-processamento de Imagens de Retinas.
Jaskirat Kaur, Deepti Mittal & Ruchi Singla. (2021) Diabetic Retinopathy Diagnosis Through Computer-Aided Fundus Image Analysis: A Review. Archives of Computational Methods in Engineering 29:3, pages 1673-1711.
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B. Dong, X. Wang, X. Qiang, F. Du, L. Gao, Q. Wu, G. Cao & C. Dai. (2022) A Multi-Branch Convolutional Neural Network for Screening and Staging of Diabetic Retinopathy Based on Wide-Field Optical Coherence Tomography Angiography. IRBM.
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Nicole Hallett, Chris Hodge, Jing Jing You, Yu Guang Wang & Gerard Sutton. 2022. Keratoconus. Keratoconus 275 289 .
Zhao-xia Lu, Peng Qian, Dan Bi, Zhe-wei Ye, Xuan He, Yu-hong Zhao, Lei Su, Si-liang Li & Zheng-long Zhu. (2021) Application of AI and IoT in Clinical Medicine: Summary and Challenges. Current Medical Science 41:6, pages 1134-1150.
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Vasudevan Lakshminarayanan, Hoda Kheradfallah, Arya Sarkar & Janarthanam Jothi Balaji. (2021) Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey. Journal of Imaging 7:9, pages 165.
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V. V. Klimontov, V. B. Berikov & O. V. Saik. (2021) Artificial intelligence in diabetology. Diabetes mellitus 24:2, pages 156-166.
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