This is an open access article distributed under the CC BY 4.0
Volume 19 article 799 pages: 344-355
This paper proposes an algorithm for synthesizing a neural network (NN) structure to analyze complex structured, low
entropy, ocular fundus images, characterized by iterative tuning of the adaptive model’s solver modules. This algorithm
will assist in synthesizing models of NNs that meet the predetermined characteristics of the classification quality.
The relevance of automating the process of ocular diagnostics of fundus pathologies is due to the need to develop domestic
medical decision-making systems. Because of using the developed algorithm, the NN structure is synthesized,
which will include two solver modules, and is intended to classify the dual-alternative information. Automated hybrid
NN structures for intelligent segmentation of complex structured, low entropy, retinal images should provide increased
efficiency of ocular diagnostics of fundus pathologies, reduce the burden on specialists, and decrease the negative
impact of the human factor in diagnosis.
Some results of this work were obtained under the Grant
Agreement in the form of subsidies from the federal budget
of the Russian Federation for state support of establishment
and development of world-class scientific
centers performing scientific research and development
under the priorities of scientific and technological development
(internal number 00600/2020 / 56890) of November
13, 2020, No. 075-15-2020-929.
1. Mao, J., Zhao, H.D., Yao, J.J. (2011). Application and prospect of artificial neural network. Electronic Design Engineering, vol. 19, no. 24, 62-65.
2. Lee, S., Oh, H.-J. (2011). Application of Artificial Neural Network for Mineral Potential Mapping. Artificial Neural Networks - Application. DOI:10.5772/16187.
3. Cheng, S., Gao, Y., Cao, J., Guo, Y., Du, Y., Hu, S. (2020). Application of Neural Network in Performance Evaluation of Satellite Communication System: Review and Prospect. Artificial Intelligence in China, 239–244. DOI:10.1007/978-981-15-0187-6_27.
4. Bhar, K.K., Bakshi, S. (2020). Application of artificial neural network for predicting water levels in Hooghly estuary, India. H2Open Journal, vol. 3, no. 1, 401–415. DOI:10.2166/h2oj.2020.041.
5. Alexandrovich, A.I., Sergeevich, M.M., Vladimirovich, O.A. (2020). Application of neural simulation methods for technological parameters identification of composite products injection molding process. Journal of Applied Engineering Science, vol. 18, no. 2, 165-172, DOI: 10.5937/jaes18-25912.
6. Golovatov, D.A., Tatarkanov, A.A., Shavaev, A.A., Gusev, S.A. (2019). The Use of Modern Information Technology in Predicting the Process of Impregnating Composite Preforms with Polymer Resins. 2019 International Conference “Quality Management, Transport and Information Security, Information Technologies” (IT&QM&IS).
7. Folgieri, R., Baldigara, T., Mamula, M. (2017). Artificial neural networks-based econometric models for tourism demand forecasting. Tourism in South East Europe, vol. 4, 169-182.
8. Nakhushev, R. S., & Sukhanova, N. V. (2020). Application of the Neural Networks for Cryptographic Information Security. 2020 International Conference Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS), pp. 421-423.
9. Dunne, R.A. (2007). A statistical approach to neural networks for pattern recognition (Vol. 702). John Wiley & Sons.
10. Yousef, M., Hussain, K.F., Mohammed, U.S. (2020). Accurate, data-efficient, unconstrained text recognition with convolutional neural networks. Pattern Recognition, vol. 108, 107482. DOI:10.1016/j.patcog.2020.107482.
11. Cevikalp, H., Benligiray, B., Gerek, O.N. (2020). Semi-supervised robust deep neural networks for multi-label image classification. Pattern Recognition, vol. 100, 107164. DOI:10.1016/j.patcog.2019.107164.
12. Chen, W., Shi, K. (2021). Multi-scale Attention Convolutional Neural Network for time series classification. Neural Networks, vol. 136, 126–140. DOI:10.1016/j.neunet.2021.01.001.
13. Mandziuk, J., Zychowski, A. (2019). Dimensionality Reduction in Multilabel Classification with Neural Networks. 2019 International Joint Conference on Neural Networks (IJCNN). DOI:10.1109/ijcnn.2019.8852156.
14. Maglogiannis, I., Zafiropoulos, E., Kyranoudis, C. (2006). Intelligent segmentation and classification of pigmented skin lesions in dermatological images. In Hellenic Conference on Artificial Intelligence, pp. 214-223.
15. Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Liang, J. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging, vol. 35, no. 5, 1299-1312.
16. Lu, L., Zheng, Y., Carneiro, G., Yang, L. (2017). Deep learning and convolutional neural networks for medical image computing. Advances in Computer Vision and Pattern Recognition, vol. 10, 978-983.
17. Anwar, S.M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., Khan, M.K. (2018). Medical image analysis using convolutional neural networks: a review. Journal of medical systems, vol. 42, no. 11, 1-13.
18. Karani, N., Erdil, E., Chaitanya, K., Konukoglu, E. (2021). Test-time adaptable neural networks for robust medical image segmentation. Medical Image Analysis, vol. 68, 101907. DOI:10.1016/j.media.2020.101907.
19. Valverde, J.M., Shatillo, A., De Feo, R., Gröhn, O., Sierra, A., Tohka, J. (2020). RatLesNetv2: A Fully Convolutional Network for Rodent Brain Lesion Segmentation. Frontiers in neuroscience, vol. 14, 1333.
20. Sheptunov, S.A., Sukhanova, N.V. (2020). The Problems of Design and Application of Switching Neural Networks in Creation of Artificial Intelligence. 2020 International Conference Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS), pp. 428-431.
21. Badaev, F.I., Filippovskaya, T.V. (2019). Health digitalization alternative: is there one or not? Proceedings of the International Scientific and Practical Conference on Digital Economy (ISCDE 2019). DOI: 10.2991/iscde-19.2019.28.
22. Gorelov, V.A., Linskaya, E.Y., Tatarkanov, A.A., Alexandrov, I.A., Sheptunov, S.A. (2020). Complex Methodological Approach to Introduction of Modern Telemedicine Technologies into the Healthcare System on Federal, Regional and Municipal Levels. 2020 International Conference Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS). DOI:10.1109/itqmis51053.2020.9322864.
23. Aversa, P., Cabantous, L., Haefliger, S. (2018). When decision support systems fail: Insights for strategic information systems from Formula 1. The Journal of Strategic Information Systems, vol. 27, no. 3, 221-236.
24. Syeda-Mahmood, T. (2015). Plenary talk: the role of machine learning in clinical decision support. SPIE Newsroom. DOI:10.1117/2.3201503.29.
25. Yegnanarayana, B. (2009). Artificial neural networks. PHI Learning Pvt. Ltd.
26. Wang, J., Liu, T., Wang, X. (2020). Human hand gesture recognition with convolutional neural networks for K-12 double-teachers instruction mode classroom. Infrared Physics & Technology, vol. 111, 103464.
27. Deperlioglu, O., Kose, U. (2018). Diabetes Determination Using Retraining Neural Network. 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). DOI:10.1109/idap.2018.8620792.
28. Sifaoui, A., Abdelkrim, A., Benrejeb, M. (2008). On the use of neural network as a universal approximator. Int. J. Sci. Tech. Control Comput. Eng, vol. 2, 386-399.
29. Izadbakhsh, A., Khorashadizadeh, S. (2020). Robust adaptive control of robot manipulators using Bernstein polynomials as universal approximator. International Journal of Robust and Nonlinear Control, vol. 30, no. 7, 2719-2735.
30. Sadr, H., Pedram, M.M., Teshnehlab, M. (2020). Multi-view deep network: A deep model based on learning features from heterogeneous neural networks for sentiment analysis. IEEE Access, vol. 8, 86984-86997.
31. Kůrková, V. (1992). Kolmogorov's theorem and multilayer neural networks. Neural networks, vol. 5, no. 3, 501-506.
32. Diaconis, P., Shahshahani, M. (1984). On nonlinear functions of linear combinations. SIAM Journal on Scientific and Statistical Computing, vol. 5, no. 1, 175-191, DOI: 10.1137/0905013.
33. Akashi, S. (2001). Application of ϵ-entropy theory to Kolmogorov—Arnold representation theorem. Reports on Mathematical Physics, vol. 48, no. 1-2, 19-26, DOI: 10.1016/s0034-4877(01)80060-4.
34. Braun, J., Griebel, M. (2009). On a constructive proof of Kolmogorov’s superposition theorem. Constructive Approximation, vol. 30, no. 3, 653-675, DOI: 10.1007/s00365-009-9054-2.
35. Karch, P., Zolotova, I. (2010). An experimental comparison of modern methods of segmentation. In 2010 IEEE 8th International Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 247-252.
36. Andersson, T., Lathen, G., Lenz, R., Borga, M. (2012). Modified gradient search for level set based image segmentation. IEEE Transactions on Image Processing, vol. 22, no. 2, 621-630.
37. Su, J., Vargas, D.V., Sakurai, K. (2019). One pixel attack for fooling deep neural networks. IEEE Transactions on Evolutionary Computation, vol. 23, no. 5, 828-841.
38. Kover, J. (2007). Perturbations by norm attaining operators. Quaestiones Mathematicae, vol. 30, no. 1, 27-33.
39. Jannati, M., Hosseinian, S.H., Vahidi, B., Li, G.J. (2016). ADALINE (ADAptive Linear NEuron)-based coordinated control for wind power fluctuations smoothing with reduced BESS (battery energy storage system) capacity. Energy, vol. 101, 1-8.
40. Yannuzzi, L.A., Rohrer, K.T., Tindel, L.J., Sobel, R.S., Costanza, M.A. (1986). Fluorescein angiography complication survey. Ophthalmology, vol. 93, no. 5, 611-617.
41. Mahmood, M., Al-Kubaisy, W.J., Al-Khateeb, B. (2019). Using artificial neural network for multimedia information retrieval. Journal of Southwest Jiaotong University, vol. 54, no. 3, DOI: 10.35741/issn.0258-27184.108.40.206.
42. Anwer, D.A. (2020). The impact of neural network techniques in the optimization of the image processing. Journal of Southwest Jiaotong University, vol. 55, no. 2, DOI: 10.35741/issn.0258-27220.127.116.11.
43. Binghai Z., Zhexin, Z. (2020). Dynamic scheduling of material delivery based on neural network and knowledge base. Journal of Hunan University Natural Sciences, vol. 47, no. 4, 1-9, DOI: 10.16339/j.cnki.hdxbzkb.2020.04.001.