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Automated Retinal Images Quality Assessment Using Machine Learning
 Mikheda, Vladislav ; Vaško, Marek (referee) ; Kavetskyi, Andrii (advisor)
This work focuses on solving the problem of retinal image quality assessment. When diagnosing a disease, physicians focus on the quality of individual anatomical structures of the retina, according to which the diagnosis is made. The aim of this work is to design and implement a program for automated quality assessment of retinal images based on anatomical structures using neural networks. Overall six neural networks were developed and implemented to solve the abovementioned problem. Three of them were to segment individual anatomical structures of the retina, and three others were meant to evaluate images based on the quality of the segmented structure. Testing of each neural network separately, as well as testing of the entire program, was performed. The model allows the evaluation of the quality of retinal images based on the anatomical structures.
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