Chinese Journal of Lasers, Volume. 49, Issue 11, 1107001(2022)
Fundus Image Screening for Diabetic Retinopathy
[1] Zhang P F, Zhang T W, Song W Y et al. Review of advances in ophthalmic optical imaging technologies from several mouse retinal imaging methods[J]. Chinese Journal of Lasers, 47, 0207003(2020).
[2] Zhang Y, Ni J S, Zhang Y Z et al. Tissue intrinsic fluorescence spectrum recovery algorithm and its application in diabetes screening[J]. Chinese Journal of Lasers, 45, 0707001(2018).
[3] Dutta S, Manideep B C, Basha S M et al. Classification of diabetic retinopathy images by using deep learning models[J]. International Journal of Grid and Distributed Computing, 11, 99-106(2018).
[4] Li Z X, Keel S, Liu C et al. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs[J]. Diabetes Care, 41, 2509-2516(2018).
[5] Wang L, Chen C X, Fu X et al. Retinal vessel segmentation of prematurity infants based on FDMU-net[J]. Laser & Optoelectronics Progress, 58, 1417003(2021).
[6] 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[J]. JAMA, 316, 2402-2410(2016).
[7] Pang H, Wang Z. Deep learning model for diabetic retinopathy detection[J]. Journal of Software, 28, 3018-3029(2017).
[8] Li Z, Keel S, Liu C et al. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs[J]. Diabetes Care, 41, 2509-2516(2018).
[9] Sopharak A, Uyyanonvara B, Barman S. Automatic exudate detection from non-dilated diabetic retinopathy retinal images using fuzzy C-means clustering[J]. Sensors, 9, 2148-2161(2009).
[10] Pan C, Li J, Wang Y et al. Collaborative learning for hyperspectral image classification[J]. Neurocomputing, 275, 2512-2524(2018).
[12] Antal B, Hajdu A. An ensemble-based system for microaneurysm detection and diabetic retinopathy grading[J]. IEEE Transactions on Biomedical Engineering, 59, 1720-1726(2012).
[13] Lin Z, Guo R, Wang Y et al. A framework for identifying diabetic retinopathy based on anti-noise detection and attention-based fusion[C], 74-82(2018).
[14] Zhou Y, He X, Huang L et al. Collaborative learning of semi-supervised segmentation and classification for medical images[C], 2079-2088(2019).
[16] Wang Q L, Wu B G, Zhu P F et al. ECA-net: efficient channel attention for deep convolutional neural networks[C], 11531-11539(2020).
[17] Ming S, Xie K P, Lei X et al. Evaluation of a novel artificial intelligence-based screening system for diabetic retinopathy in community of China: a real-world study[J]. International Ophthalmology, 41, 1291-1299(2021).
[18] Ruamviboonsuk P, Krause J, Chotcomwongse P et al. Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program[J]. Npj Digital Medicine, 2, 25(2019).
[19] Natarajan S, Jain A, Krishnan R et al. Diagnostic accuracy of community-based diabetic retinopathy screening with an offline artificial intelligence system on a smartphone[J]. JAMA Ophthalmology, 137, 1182-1188(2019).
[20] Abràmoff M D, Lou Y Y, Erginay A et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning[J]. Investigative Ophthalmology & Visual Science, 57, 5200-5206(2016).
[21] Knight J C, Nowotny T. GPUs outperform current HPC and neuromorphic solutions in terms of speed and energy when simulating a highly-connected cortical model[J]. Frontiers in Neuroscience, 12, 941(2018).
[22] Yuan K, Huo L. Multiple-scale inpainting convolutional neural network for retinal OCT image segmentation[J]. Chinese Journal of Lasers, 48, 1507004(2021).
[23] Aswathy A L, Anand H S, Vinod C S S. COVID-19 diagnosis and severity detection from CT-images using transfer learning and back propagation neural network[J]. Journal of Infection and Public Health, 14, 1435-1445(2021).
[24] Cao J F, Yan M M, Jia Y M et al. Application of a modified Inception-v3 model in the dynasty-based classification of ancient murals[J]. EURASIP Journal on Advances in Signal Processing, 2021, 49(2021).
[25] Yang Y J, Cho B J, Lee M J et al. Automated classification of colorectal neoplasms in white-light colonoscopy images via deep learning[J]. Journal of Clinical Medicine, 9, 1593(2020).
[26] Vidyarthi A, Malik A. A hybridized modified densenet deep architecture with CLAHE algorithm for humpback whale identification and recognition[J]. Multimedia Tools and Applications, 1-15(2021).
Get Citation
Copy Citation Text
Jiayu Li, Minghui Chen, Ruijun Yang, Wenfei Ma, Xiangling Lai, Duowen Huang, Duxin Liu, Xinhong Ma, Yue Shen. Fundus Image Screening for Diabetic Retinopathy[J]. Chinese Journal of Lasers, 2022, 49(11): 1107001
Category: biomedical photonics and laser medicine
Received: Sep. 16, 2021
Accepted: Nov. 8, 2021
Published Online: Jun. 2, 2022
The Author Email: Chen Minghui (cmhui.43@163.com)