053d0b91-0073-4368-90f3-91c570248773 beie director@blueeyesintelligence.org 10.35940/ijitee.I9296.0710921 10.1109/INTERCON.2017.8079692E. V. Carrera, A. Gonzalez, and R. Carrera, "Automated detection of diabetic retinopathy using SVM," 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), 2017, pp. 1-4.10.1016/j.artmed.2019.03.009R. Pires, S. Avila, J. Wainer, E. Valle, M.D. Abramoff, A. Rocha, "A data-driven approach to referable diabetic retinopathy detection Artif Intell Med", 96 (2019), pp. 93-106.M.T. Esfahani, M. Ghaderi, R. Kafiyeh, "Classification of diabetic and normal fundus images using new deep learning method Leonardo Electron J Pract Technol", 17 (32) (2018), pp. 233-248.https://en.wikipedia.org/wiki/Gaussian\_blurhttps://www.kaggle.com/c/aptos2019-blindness-detection/dataKaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Deep Residual Learning for Image Recognition". Available: https://arxiv.org/pdf/1512.03385.pdfhttps://machinelearningmastery.com/how-to-use-transfer-learning-when-developing-convolutional-neural-network-models/https://github.com/fchollet/deep-learning-models/releases/tag/v0.1https://towardsdatascience.com/residual-networks-resnets-cb474c7c834ahttps://blog.exsilio.com/all/accuracy-precision-recall-f1-score-interpretation-of-performance-measures/