[1] Gandhi M. and Dhanasekaran R. (2013). Diagnosis of Diabetic Retinopathy Using Morphological Process and SVM Classifier, IEEE International conference on Communication and Signal Processing, India pp: 873-877

[2] Akara S. ,BunyaritU.,SarahB.,Tom W.,Khine T. (2009)“Machine learning approach to automatic exudate detection in retinal images from diabetic patients” volume 57-issue 2.

[3] Figure 6: A detailed representation for AlexNet DCNN architecture. (n.d.). doi: 10.7717/peerj.6201/fig-6

[4] Figure 6: The fine-tuned ResNet neural network. (n.d.). doi: 10.7717/peerjcs.236/fig-6

[5] Ebrahim, M., Al-Ayyoub, M., & Alsmirat, M. A. (2019). Will Transfer Learning Enhance ImageNet Classification Accuracy Using ImageNet-Pretrained Models? 2019 10th International Conference on Information and Communication Systems (ICICS). doi: 10.1109/iacs.2019.8809114

[6] A novel image recognition approach using multiscale saliency model and GoogLeNet. (2020). Electronic Imaging. doi: 10.2352/issn.2470-1173.2020.10.ipas-097

[7] Ke, H., Chen, D., Li, X., Tang, Y., Shah, T., & Ranjan, R. (2018). Towards Brain Big Data Classification: Epileptic EEG Identification With a Lightweight VGGNet on Global MIC. IEEE Access, 6, 14722–14733. doi: 10.1109/access.2018.2810882

[8] Feature-Based Transfer Learning. (2020). Transfer Learning, 34–44. doi: 10.1017/9781139061773.005

[9] Inception V3 Trained on ImageNet Competition Data. (n.d.). Wolfram Research Data Repository. doi: 10.24097/wolfram.50858.data

[10] Sinha, D., & El-Sharkawy, M. (2019). Thin MobileNet: An Enhanced MobileNet Architecture. 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). doi: 10.1109/uemcon47517.2019.8993089

[11] Pattanayak, S. (2017). Introduction to Deep-Learning Concepts and TensorFlow. Pro Deep Learning with TensorFlow, 89–152. doi: 10.1007/978-1-4842-3096-1_2

[12] Figure 4: Deep convolutional neural network architecture. (n.d.). doi: 10.7717/peerjcs.181/fig-4

[13] 刘 哲. (2016). The Research of Feature Extraction Algorithm by Integrating T-Rank and Softmax Methods. Modeling and Simulation, 05(04), 117–124. doi: 10.12677/mos.2016.54017

[14] Alex C, Boston A. (2016).Artificial Intelligence, Deep Learning, and Neural Networks, Explained (16:n37)

[15] Boulesteix, A.-L., Janitza, S., Kruppa, J., & König, I. R. (2012). Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(6), 493–507.Jason B, Boinee P.“Machine Learning Algorithms”2(3), 138–147. Published on 15, 2016.

[16] Saimadhu P. How the Random Forest Algorithm Works in Machine Learning. Published on May 22, 2017

[17] Boser B. E, Guyon I. M.,Vapnik V. N. (1992). “A training algorithm for optimal margin classiers”.Proceedings of the 5th Annual Workshop on Computational Learning TheoryCOLT'92, 152 Pittsburgh, PA, USA. ACM Press, July 1992. On Page(s): 144-152

[18]   Fleming AD, Philip S, Goatman KA, Olson JA, Sharp PF. Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Trans Med Imaging. 2006;25:1223