Synthesis of neural network structure for the analysis of complex structured ocular fundus images

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 pr...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Tatarkanov Aslan, Alexandrov Islam, Glashev Rasul
Formato: article
Lenguaje:EN
Publicado: Institut za istrazivanja i projektovanja u privredi 2021
Materias:
T
Acceso en línea:https://doaj.org/article/3306733cf374425bbaf9aff124d49e7b
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario: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.