Laser & Optoelectronics Progress, Volume. 60, Issue 14, 1417001(2023)
Classification of Diabetic Retinopathy with Feature Fusion Network
[1] Teo Z L, Tham Y C, Yu M et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta-analysis[J]. Ophthalmology, 128, 1580-1591(2021).
[2] Prawej A, Noushin T, Nuren S N et al. Diabetic retinopathy: an overview on mechanisms, pathophysiology and pharmacotherapy[J]. Diabetology, 3, 159-175(2022).
[3] Li J Y, Chen M H, Yang R J et al. Fundus image screening for diabetic retinopathy[J]. Chinese Journal of Lasers, 49, 1107001(2022).
[4] Vujosevic S, Aldington S J, Silva P et al. Screening for diabetic retinopathy: new perspectives and challenges[J]. The Lancet Diabetes & Endocrinology, 8, 337-347(2020).
[5] Li X G, Pang T T, Xiong B et al. Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification[C](2017).
[6] Pan S J, Yang Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 22, 1345-1359(2010).
[7] Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 20, 273-297(1995).
[8] Rocha A, Carvalho T, Jelinek H F et al. Points of interest and visual dictionaries for automatic retinal lesion detection[J]. IEEE Transactions on Biomedical Engineering, 59, 2244-2253(2012).
[9] Decencière E, Zhang X W, Cazuguel G et al. Feedback on a publicly distributed image database: the messidor database[J]. Image Analysis & Stereology, 33, 231-234(2014).
[10] Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning[J]. Ophthalmology, 124, 962-969(2017).
[11] 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).
[12] Cuadros J, Bresnick G. EyePACS: an adaptable telemedicine system for diabetic retinopathy screening[J]. Journal of Diabetes Science and Technology, 3, 509-516(2009).
[13] Krishnan A S, Clive R D, Bhat V et al. A transfer learning approach for diabetic retinopathy classification using deep convolutional neural networks[C](2018).
[14] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).
[15] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions[C](2015).
[17] Li Y H, Yeh N N, Chen S J et al. Computer-assisted diagnosis for diabetic retinopathy based on fundus images using deep convolutional neural network[J]. Mobile Information Systems, 2019, 6142839(2019).
[18] Amalia R, Bustamam A, Sarwinda D. Detection and description generation of diabetic retinopathy using convolutional neural network and long short-term memory[J]. Journal of Physics: Conference Series, 1722, 012010(2021).
[19] Greff K, Srivastava R K, Koutník J et al. LSTM: a search space odyssey[J]. IEEE Transactions on Neural Networks and Learning Systems, 28, 2222-2232(2017).
[20] Yang H G, Chen J J, Xu M F. Fundus disease image classification based on improved transformer[C], 207-214(2021).
[23] Sun R, Li Y H, Zhang T Z et al. Lesion-aware transformers for diabetic retinopathy grading[C], 10933-10942(2021).
[24] Bodapati J D, Naralasetti V, Shareef S N et al. Blended multi-modal deep ConvNet features for diabetic retinopathy severity prediction[J]. Electronics, 9, 914(2020).
[26] Kassani S H, Kassani P H, Khazaeinezhad R et al. Diabetic retinopathy classification using a modified xception architecture[C](2019).
[27] Chollet F. Xception: deep learning with depthwise separable convolutions[C], 1800-1807(2017).
[28] Tan M, Le Q V. Efficientnet: rethinking model scaling for convolutional neural networks[C], 6105-6114(2019).
[29] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).
[30] Dai Y M, Gieseke F, Oehmcke S et al. Attentional feature fusion[C], 3559-3568(2021).
[31] Zhang J, Cao Y, Wang Y et al. Fully point-wise convolutional neural network for modeling statistical regularities in natural images[C], 984-992(2018).
[32] Newey W K. Adaptive estimation of regression models via moment restrictions[J]. Journal of Econometrics, 38, 301-339(1988).
[34] Zhang H T. The research of deep learning model for diabetic retinopathy grading[D](2021).
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Shuang Zhao, Ge Mu, Wenhua Zhao, Zhiqing Ma. Classification of Diabetic Retinopathy with Feature Fusion Network[J]. Laser & Optoelectronics Progress, 2023, 60(14): 1417001
Category: Medical Optics and Biotechnology
Received: Aug. 29, 2022
Accepted: Sep. 23, 2022
Published Online: Jul. 17, 2023
The Author Email: Zhao Wenhua (zhaowh0621@163.com)