Abstract
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.
Highlights
Rasul GlashevCite article: Aslan, T., Islam A., & Rasul G
Over recent decades, artificial neural network (NN) have gained wide practical applications in completely different interdisciplinary and nontrivial areas, namely: identification of the composition and prediction of the properties of new compounds and materials; management of technological processes and product quality control; environmental assessment and management of natural resources; assessment and forecasting of economic parameters, both at the level of an individual product entering the market, and within the operation of an entire enterprise, or a group of enterprises; sociodynamic and econometric modeling; and predictive medicine [1,2,3,4,5,6,7,8]
As the first solver module is configured for the "segment" class, its structure should correspond to the universal structure described by equation (13)
Summary
Cite article: Aslan, T., Islam A., & Rasul G. Synthesis of neural network structure for the analysis of complex structured ocular fundus images. Journal of Applied Engineering Science, 19(2), 344 - 355. Online aceess of full paper is available at: www.engineeringscience.rs/browse-issues
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