References:
1. Oksuz, I., Clough, J.R., Ruijsink, B., et al. Deep learning-based detection and correction of cardiac MR motion artefacts during reconstruction for highquality segmentation", IEEE Transactions on Medical Imaging, 39(12), pp. 4001-4010 (2020).
2. Haan, K., Rivenson, Y., Wu, Y., et al. "Deep-learningbased image reconstruction and enhancement in optical microscopy", Proceedings of the IEEE, 108(1), pp. 30-50 (2020).
3. Olefir, I., Tzoumas, S., Restivo, C., et al. "Deep learning-based spectral unmixing for optoacoustic imaging of tissue oxygen saturation", IEEE Transactions on Medical Imaging, 39(11), pp. 3643-3654 (2020).
4. Liu, J., Pan, Y., Min, L., et al. "Applications of deep learning to MRI images: A survey", Big Data Mining and Analytics, 1(1), pp. 1-18 (2018).
5. Li, L.F., Wang, X., Hu, W.J., et al. "Deep learning in skin disease image recognition: A review", IEEE Access, 8, pp. 208264-208280 (2020).
6. Wang, J., Bai, Y., and Xia, B. "Simultaneous diagnosis of severity and features of diabetic retinopathy in fundus photography using deep learning", IEEE Journal of Biomedical and Health Informatics, 24(12), pp. 3397-3407 (2020).
7. Seo, H., Bassenne, M., and Xing, L. "Closing the gap between deep neural network modeling and biomedical decision-making metrics in segmentation via adaptive loss functions", IEEE Transactions on Medical Imaging, 40(2), pp. 585-593 (2021).
8. Schlemper, J., Caballero, J., Hajnal, J.V., et al. "A deep cascade of convolutional neural networks for dynamic MR image reconstruction", IEEE Transactions on Medical Imaging, 37(2), pp. 491-503 (2018).
9. Xue, Y., Li, N., Wei, N., et al. "Deep learning-based earlier detection of esophageal cancer using improved empirical wavelet transform from endoscopic image", IEEE Access, 8, pp. 123765-123772 (2020).
10. Noreen, N., Palaniappan, S., Qayyum, A., et al. "A deep learning model based on concatenation approach for the diagnosis of brain tumor", IEEE Access, 8, pp. 55135-55144 (2020).
11. Wei, L., Ding, K., and Hu, H. "Automatic skin cancer detection in dermoscopy images based on ensemble fllightweight deep learning network", IEEE Access, 8, pp. 99633-99647 (2020).
12. Qiao, L., Zhu, Y., and Zhou, H. "Diabetic retinopathy detection using prognosis of microaneurysm and early diagnosis system for non-proliferative diabetic retinopathy based on deep learning algorithms", IEEE Access, 8, pp. 104292-104302 (2020).
13. Li, X., Hu, X., Yu, L., et al. "CANet: Cross-disease attention network for joint diabetic retinopathy and diabetic macular edema grading", IEEE Transactions on Medical Imaging, 39(5), pp. 1483-1493 (2020).
14. Zhu, S., Liu, H., Du, R., et al. "Tortuosity of retinal main and branching arterioles, venules in patients with type 2 diabetes and diabetic retinopathy in China", IEEE Access, 8, pp. 6201-6208 (2020).
15. Khansari, M.M. "Automated deformation-based analysis of 3D optical coherence tomography in diabetic retinopathy", IEEE Transactions on Medical Imaging, 39(1), pp. 236-245 (2020).
16. Zeng, X., Chen, H., Luo, Y., et al. "Automated diabetic retinopathy detection based on binocular siamese-like convolutional neural network", IEEE Access, 7, pp. 30744-30753 (2019).
17. Araujo, T., Aresta, G., Mendonca, L., et al. "Data augmentation for improving proliferative diabetic retinopathy detection in eye fundus images", IEEE Access, 8, pp. 182462-182474 (2020).
18. Qummar, S., Khan, F.G., Shah, S., et al. "A deep learning ensemble approach for diabetic retinopathy detection", IEEE Access, 7, pp. 150530-150539 (2019).
19. Sun, Y. "The neural network of one-dimensional convolution-an example of the diagnosis of diabetic retinopathy", IEEE Access, 7, pp. 69657-69666 (2019).
20. McCulloch, W.S. and Pitts, W. "A logical calculus of the ideas immanent in nervous activity", The Bulletin of Mathematical Biophysics, 5(4), pp. 115-133 (1943).
21. Haykin, S.S., Neural Networks and Learning Machines- 3, Pearson Upper Saddle River (2009).
22. Ivakhnenko, A.G. and. Lapa, V.G. "Cybernetic predicting devices", CCM Information Corporation (1965).
23. Fukushima, K. "Neocognitron: A hierarchical neural network capable of visual pattern recognition", Neural Networks, 1(2), pp. 119-130 (1988).
24. Weng, J., Cohen, P., and Herniou, M. "Camera calibration with distortion models and accuracy evaluation", IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(10), pp. 965-980 (1992).
25. Cortes, C. and Vapnik, V. "Support-vector networks", Machine Learning, 20(3), pp. 273-297 (1995).
26. Hochreiter, S. and Schmidhuber, J. "Long short-term memory", Neural Computation, 9(8), pp. 735-1780 (1997).
27. Hinton, G.E., Srivastava, N., Krizhevsky, A., et al. "Improving neural networks by preventing coadaptation of feature detectors", Arxiv.net, pp. 1-18 (2016).
28. Lohr, S., The Age of Big Data, New York Times (2012).
29. Taigman, Y., Yang, M., Ranzato, M., et al. "Deepface: Closing the gap to human-level performance in face verification", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701- 1708 (2014).
30. Lee, K. and Son, M. "Deepspotcloud: Leveraging cross-region GPU spot instances for deep learning", 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 98-105 (2017).
31. Tokui, S., Oono, K., Hido, S., et al. "Chainer: A next-generation open source framework for deep learning, in Proceedings of workshop on machine learning systems", The Twenty-Ninth Annual Conference on Neural Information Processing Systems, 5, pp. 1-6 (2015).
32. Dai, M., Xiao, G., Fiondella, L., et al. "Deep learningenabled resolution-enhancement in mini- and regular microscopy for biomedical imaging", Sens Actuators A Phys., 1(331), 112928 (2021).
33. Yahia, S., Said, S., and Zaied, M. "Wavelet extreme learning machine and deep learning for data classification", Neurocomputing, 470, pp. 280-289 (2021).
34. Valarmathi, S. and Vijayabhanu, R. "A survey on diabetic retinopathy disease detection and classification using deep learning techniques", 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), Chennai, India, pp. 1-4 (2021).
35. Zou, J. and Zhang, Q. "EyeSay: Eye electrooculography decoding with deep learning", 2021 IEEE International Conference on Consumer Electronics, USA, pp. 1-3 (2021).
36. Khan, I.A., Sajeeb, A., and Fattah, S.A. "An automatic ocular disease detection scheme from enhanced fundus images based on ensembling deep CNN networks", 11th International Conference on Electrical and Computer Engineering, Dhaka, Bangladesh, pp. 491-494 (2020).
37. Haque, R.U., Pongos, A.L., Manzanares, C.M., et al. "Deep convolutional neural networks and transfer learning for measuring cognitive impairment using eyetracking in a distributed tablet-based environment", IEEE Transactions on Biomedical Engineering, 68(1), pp. 11-18 (2021).
38. Stefan, A.M., Paraschiv, E.A., Ovreiu S., et al. "A review of lgaucoma detection from digital fundus images using machine learning techniques", International Conference on e-Health and Bioengineering, Iasi, Romania, pp. 1-4 (2020).
39. Mehmood, T., Alfonso E., Gerevini, A.L., et al. "Combining multi-task learning with transfer learning for biomedical named entity recognition", Procedia Computer Science, 176, pp. 848-857 (2020).
40. Wu, X., Chen, S., Huang, J., et al. "DDeep3M: Docker-powered deep learning for biomedical image segmentation", Journal of Neuroscience Methods, 342, pp. 804-808 (2020).
41. Tian, Y. and Fu, S. "A descriptive framework for the field of deep learning applications in medical images", Knowledge-Based Systems, 210, pp. 106-445 (2020).
42. Image dataset, "https://www.kaggle.com/datasets",[accessed:21.02.2021]. 43. Pre-trained model and dockerfile, "https://github. com/itopaloglu/Artificial Intelligence" [accessed:25. 02.2021].