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Lotfy, J. Adeghate, H. Kalasz, J. Singh, and E. Adeghate, \u201cChronic complications of diabetes mellitus: A mini review,\u201d Curr. Diabetes Rev., vol.13, no.1, pp.3-10, 2017. 10.2174\/1573399812666151016101622","DOI":"10.2174\/1573399812666151016101622"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] W.L. Alyoubi, W.M. Shalash, and M.F. Abulkhair, \u201cDiabetic retinopathy detection through deep learning techniques: A review,\u201d Informatics in Medicine Unlocked, vol.20, 100377, 2020. 10.1016\/j.imu.2020.100377","DOI":"10.1016\/j.imu.2020.100377"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] L. Dai, B. Sheng, T. Chen, Q. Wu, R. Liu, C. Cai, L. Wu, D. Yang, H. Hamzah, Y. Liu, X. Wang, Z. Guan, S. Yu, T. Li, Z. Tang, A. Ran, H. Che, H. Chen, Y. Zheng, J. Shu, S. Huang, C. Wu, S. Lin, D. Liu, J. Li, Z. Wang, Z. Meng, J. Shen, X. Hou, C. Deng, L. Ruan, F. Lu, M. Chee, T.C. Quek, R. Srinivasan, R. Raman, X. Sun, Y.X. Wang, J. Wu, H. Jin, R. Dai, D. Shen, X. Yang, M. Guo, C. Zhang, C.Y. Cheung, G.S.W. Tan, Y.-C. Tham, C.-Y. Cheng, H. Li, T.Y. Wong, and W. Jia, \u201cA deep learning system for predicting time to progression of diabetic retinopathy,\u201d Nature Medicine, vol.30, no.2, pp.584-594, 2024. 10.1038\/s41591-023-02702-z","DOI":"10.1038\/s41591-023-02702-z"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, \u201cA survey of convolutional neural networks: Analysis, applications, and prospects,\u201d IEEE Trans. Neural Netw. Learn. Syst., vol.33, no.12, pp.6999-7019, 2021. 10.1109\/tnnls.2021.3084827","DOI":"10.1109\/TNNLS.2021.3084827"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] H. Pratt, F. Coenen, D.M. Broadbent, S.P. Harding, and Y. Zheng, \u201cConvolutional neural networks for diabetic retinopathy,\u201d Procedia Computer Science, vol.90, pp.200-205, 2016. 10.1016\/j.procs.2016.07.014","DOI":"10.1016\/j.procs.2016.07.014"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] S. Gayathri, V.P. Gopi, and P. Palanisamy, \u201cA lightweight CNN for diabetic retinopathy classification from fundus images,\u201d Biomedical Signal Processing and Control, vol.62, 102115, 2020. 10.1016\/j.bspc.2020.102115","DOI":"10.1016\/j.bspc.2020.102115"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] S. Gayathri, V.P. Gopi, and P. Palanisamy, \u201cDiabetic retinopathy classification based on multipath CNN and machine learning classifiers,\u201d Physical and Engineering Sciences in Medicine, vol.44, no.3, pp.639-653, 2021. 10.1007\/s13246-021-01012-3","DOI":"10.1007\/s13246-021-01012-3"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] K. Ashwini and R. Dash, \u201cGrading diabetic retinopathy using multiresolution based CNN,\u201d Biomedical Signal Processing and Control, vol.86, 105210, 2023. 10.1016\/j.bspc.2023.105210","DOI":"10.1016\/j.bspc.2023.105210"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] S. Khan, M. Naseer, M. Hayat, S.W. Zamir, F.S. Khan, and M. Shah, \u201cTransformers in vision: A survey,\u201d ACM Computing Surveys (CSUR), vol.54, no.10s, pp.1-41, 2022. 10.1145\/3505244","DOI":"10.1145\/3505244"},{"key":"10","unstructured":"[10] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, \u201cAn image is worth 16x16 words: Transformers for image recognition at scale,\u201d International Conference on Learning Representations, 2021."},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] T. Karkera, C. Adak, S. Chattopadhyay, and M. Saqib, \u201cDetecting severity of diabetic retinopathy from fundus images: A transformer network-based review,\u201d Neurocomputing, vol.597, 127991, 2024. 10.1016\/j.neucom.2024.127991","DOI":"10.1016\/j.neucom.2024.127991"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] J. Wu, R. Hu, Z. Xiao, J. Chen, and J. Liu, \u201cVision transformer-based recognition of diabetic retinopathy grade,\u201d Medical Physics, vol.48, no.12, pp.7850-7863, 2021. 10.1002\/mp.15312","DOI":"10.1002\/mp.15312"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] Z. Wang, H. Lu, H. Yan, H. Kan, and L. Jin, \u201cVison transformer adapter-based hyperbolic embeddings for multi-lesion segmentation in diabetic retinopathy,\u201d Scientific Reports, vol.13, no.1, 11178, 2023. 10.1038\/s41598-023-38320-5","DOI":"10.1038\/s41598-023-38320-5"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] W. Nazih, A.O. Aseeri, O.Y. Atallah, and S. El-Sappagh, \u201cVision transformer model for predicting the severity of diabetic retinopathy in fundus photography-based retina images,\u201d IEEE Access, vol.11, pp.117546-117561, 2023. 10.1109\/access.2023.3326528","DOI":"10.1109\/ACCESS.2023.3326528"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] K. Ohri, M. Kumar, and D. Sukheja, \u201cSelf-supervised approach for diabetic retinopathy severity detection using vision transformer,\u201d Progress in Artificial Intelligence, vol.13, pp.165-183, 2024. 10.1007\/s13748-024-00325-0","DOI":"10.1007\/s13748-024-00325-0"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] W.S. McCulloch and W. Pitts, \u201cA logical calculus of ideas immanent in nervous activity,\u201d Bulletin of Mathematical Biophysics, vol.5, pp.115-133, 1943. 10.1007\/bf02478259","DOI":"10.1007\/BF02478259"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] H.T. \u00dcnal and F. Ba\u015f\u00e7ift\u00e7i, \u201cNeural logic circuits: An evolutionary neural architecture that can learn and generalize,\u201d Knowledge-Based Systems, vol.265, 110379, 2023. 10.1016\/j.knosys.2023.110379","DOI":"10.1016\/j.knosys.2023.110379"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] S. Gao, M. Zhou, Y. Wang, J. Cheng, H. Yachi, and J. Wang, \u201cDendritic neuron model with effective learning algorithms for classification, approximation, and prediction,\u201d IEEE Trans. Neural Netw. Learn. Syst., vol.30, no.2, pp.601-614, 2019. 10.1109\/tnnls.2018.2846646","DOI":"10.1109\/TNNLS.2018.2846646"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] Y. Ding, J. Yu, C. Gu, S. Gao, and C. Zhang, \u201cA multi-in and multi-out dendritic neuron model and its optimization,\u201d Knowledge-Based Systems, vol.286, 111442, 2024. 10.1016\/j.knosys.2024.111442","DOI":"10.1016\/j.knosys.2024.111442"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] S. Gao, M. Zhou, Z. Wang, D. Sugiyama, J. Cheng, J. Wang, and Y. Todo, \u201cFully complex-valued dendritic neuron model,\u201d IEEE Trans. Neural Netw. Learn. Syst., vol.34, no.4, pp.2105-2118, 2023. 10.1109\/tnnls.2021.3105901","DOI":"10.1109\/TNNLS.2021.3105901"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] Y. Yu, Z. Lei, Y. Wang, T. Zhang, C. Peng, and S. Gao, \u201cImproving dendritic neuron model with dynamic scale-free network-based differential evolution,\u201d IEEE\/CAA Journal of Automatica Sinica, vol.9, no.1, pp.99-110, 2022. 10.1109\/jas.2021.1004284","DOI":"10.1109\/JAS.2021.1004284"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] Z. Liu, Z. Zhang, Z. Lei, M. Omura, R.-L. Wang, and S. Gao, \u201cDendritic deep learning for medical segmentation,\u201d IEEE\/CAA Journal of Automatica Sinica, vol.11, no.3, pp.803-805, 2024. 10.1109\/jas.2023.123813","DOI":"10.1109\/JAS.2023.123813"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] J. Li, Z. Lei, Z. Zhang, H. Li, Y. Todo, and S. Gao, \u201cAlternating excitation-inhibition dendritic computing for classification,\u201d IEEE Trans. Artif. Intell., vol.5, no.11, pp.5431-5441, 2024. 10.1109\/tai.2024.3416236","DOI":"10.1109\/TAI.2024.3416236"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] E. Egrioglu, E. Ba\u015f, and M.-Y. Chen, \u201cRecurrent dendritic neuron model artificial neural network for time series forecasting,\u201d Information Sciences, vol.607, pp.572-584, 2022. 10.1016\/j.ins.2022.06.012","DOI":"10.1016\/j.ins.2022.06.012"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] Z. Xu, Z. Wang, J. Li, T. Jin, X. Meng, and S. Gao, \u201cDendritic neuron model trained by information feedback-enhanced differential evolution algorithm for classification,\u201d Knowledge-Based Systems, vol.233, 107536, 2021. 10.1016\/j.knosys.2021.107536","DOI":"10.1016\/j.knosys.2021.107536"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] H. He, S. Gao, T. Jin, S. Sato, and X. Zhang, \u201cA seasonal-trend decomposition-based dendritic neuron model for financial time series prediction,\u201d Applied Soft Computing, vol.108, 107488, 2021. 10.1016\/j.asoc.2021.107488","DOI":"10.1016\/j.asoc.2021.107488"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] J. Li, Z. Liu, R.-L. Wang, and S. Gao, \u201cDendritic deep residual learning for COVID-19 prediction,\u201d IEEJ Trans. Electr. Electron. Eng., vol.18, no.2, pp.297-299, 2023. 10.1002\/tee.23723","DOI":"10.1002\/tee.23723"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] R.-L. Wang, Z. Lei, Z. Zhang, and S. Gao, \u201cDendritic convolutional neural network,\u201d IEEJ Trans. Electr. Electron. Eng., vol.17, no.2, pp.302-304, 2022. 10.1002\/tee.23513","DOI":"10.1002\/tee.23513"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] Z. Yuan, S. Gao, Y. Wang, J. Li, C. Hou, and L. Guo, \u201cPrediction of PM2.5 time series by seasonal trend decomposition-based dendritic neuron model,\u201d Neural Computing and Applications, vol.35, no.21, pp.15397-15413, 2023. 10.1007\/s00521-023-08513-0","DOI":"10.1007\/s00521-023-08513-0"},{"key":"30","doi-asserted-by":"publisher","unstructured":"[30] E. Egrioglu and E. Bas, \u201cA new deep neural network for forecasting: Deep dendritic artificial neural network,\u201d Artificial Intelligence Review, vol.57, no.7, 171, 2024. 10.1007\/s10462-024-10790-7","DOI":"10.1007\/s10462-024-10790-7"},{"key":"31","doi-asserted-by":"publisher","unstructured":"[31] L. Zhong, Z. Liu, H. He, Z. Lei, and S. Gao, \u201cDendritic learning and miss region detection-based deep network for multi-scale medical segmentation,\u201d Journal of Bionic Engineering, vol.21, no.4, pp.2073-2085, 2024. 10.1007\/s42235-024-00499-2","DOI":"10.1007\/s42235-024-00499-2"},{"key":"32","doi-asserted-by":"publisher","unstructured":"[32] Z. Ju, Z. Liu, Y. Gao, H. Li, Q. Du, K. Yoshikawa, and S. Gao, \u201cEfficientnet empowered by dendritic learning for diabetic retinopathy,\u201d IEICE Trans. Inf. & Syst., vol.E107-D, no.9, pp.1281-1284, Sept. 2024. 10.1587\/transinf.2023EDL8080","DOI":"10.1587\/transinf.2023EDL8080"},{"key":"33","doi-asserted-by":"publisher","unstructured":"[33] Y. Wang, Y. Yu, T. Zhang, K. Song, Y. Wang, and S. Gao, \u201cImproved dendritic learning: Activation function analysis,\u201d Information Sciences, vol.679, 121034, 2024. 10.1016\/j.ins.2024.121034","DOI":"10.1016\/j.ins.2024.121034"},{"key":"34","doi-asserted-by":"publisher","unstructured":"[34] Z. Liu, Y. Song, J. Yi, Z. Zhang, M. Omura, Z. Lei, and S. Gao, \u201cDilated dendritic learning of global-local feature representation for medical image segmentation,\u201d Expert Systems with Applications, vol.264, 125874, 2025. 10.1016\/j.eswa.2024.125874","DOI":"10.1016\/j.eswa.2024.125874"},{"key":"35","doi-asserted-by":"crossref","unstructured":"[35] K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.770-778, 2016. 10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] S. Xie, R. Girshick, P. Doll\u00e1r, Z. Tu, and K. He, \u201cAggregated residual transformations for deep neural networks,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.5987-5995, 2017. 10.1109\/cvpr.2017.634","DOI":"10.1109\/CVPR.2017.634"},{"key":"37","doi-asserted-by":"crossref","unstructured":"[37] S. Zagoruyko and N. Komodakis, \u201cWide residual networks,\u201d arXiv preprint arXiv:1605.07146, 2016. 10.48550\/arXiv.1605.07146","DOI":"10.5244\/C.30.87"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[38] G. Huang, Z. Liu, L. Van Der Maaten, and K.Q. Weinberger, \u201cDensely connected convolutional networks,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.2261-2269, 2017. 10.1109\/cvpr.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"39","unstructured":"[39] M. Tan and Q.V. Le, \u201cEfficientNet: Rethinking model scaling for convolutional neural networks,\u201d International Conference on Machine Learning, pp.6105-6114, PMLR, 2019."},{"key":"40","doi-asserted-by":"crossref","unstructured":"[40] X. Zhang, X. Zhou, M. Lin, and J. Sun, \u201cShuffleNet: An extremely efficient convolutional neural network for mobile devices,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.6848-6856, 2018. 10.1109\/CVPR.2018.00716","DOI":"10.1109\/CVPR.2018.00716"},{"key":"41","unstructured":"[41] A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. 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