COMPUTER ANALYSIS OPTICAL COHERENCE TOMOGRAPHY IMAGES BY USING UNSUPERVISED MACHINE LEARNING ALGORITHM

dc.contributor.authorAmirgaliev Y.
dc.contributor.authorTastembekov A.
dc.contributor.authorBertailak Sh.
dc.date.accessioned2024-02-06T08:42:04Z
dc.date.available2024-02-06T08:42:04Z
dc.date.issued2020
dc.description.abstractAbstract. In recent years, computer image analysis has been developing rapidly. In the field of medicine has been identified to a new level that has greatly helped for the diagnostic system. There are many information systems in the field of ophthalmology and cardiology. Advanced technologies not only accelerate the work of doctors but also help to diagnose the disease in a timely manner and prescribe the treatment. In this research paper was carried out an analysis of the machine learning algorithm using a database of tomographic images of blood vessels in the eye system. Were studied the used methods for calculating several reasons in order to select a specific model, methods for calculating its properties and advantages. The main goal of this research is that doctors can not only check the current condition of the patient’s eye but also diagnose certain diseases, such as diabetes and anemia
dc.identifier.citationY. Amirgaliev , A. Tastembekov , Sh. Bertailak / COMPUTER ANALYSIS OPTICAL COHERENCE TOMOGRAPHY IMAGES BY USING UNSUPERVISED MACHINE LEARNING ALGORITHM / СДУ хабаршысы - 2020
dc.identifier.issn2709-2631
dc.identifier.urihttps://repository.sdu.edu.kz/handle/123456789/1185
dc.language.isoen
dc.publisherСДУ хабаршысы - 2020
dc.subjectlinear discriminant analysis
dc.subjectsubretinal fluid segmentation
dc.subjectlevel set
dc.subjectlocal Gaussian pre-fitting energy
dc.subjectСДУ хабаршысы - 2020
dc.subject№1
dc.titleCOMPUTER ANALYSIS OPTICAL COHERENCE TOMOGRAPHY IMAGES BY USING UNSUPERVISED MACHINE LEARNING ALGORITHM
dc.typeArticle

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