DOI: 10.5937/jaes0-31238
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.
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