基于OCT图像分析的视网膜水肿鉴别诊断

IF 1 Q4 OPTICS

Optical Memory and Neural Networks Pub Date : 2024-12-23 DOI:10.3103/S1060992X24700589

A. Yu. Ionov, N. Yu. Ilyasova, N. S. Demin, E. A. Zamytskiy

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引用次数: 0

摘要

本工作的目的是视网膜水肿的鉴别诊断，研究深度学习方法及其在图像分析中的应用。研究了卷积神经网络在视网膜分层语义分割中的应用，证明了卷积神经网络在色素上皮和视网膜两层语义分割中的有效性。实现了一种基于神经网络提取层智能分析的疾病分类器。并证明了其在视网膜水肿鉴别诊断中的适用性。疾病预测准确率达90%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Differential Diagnosis of Retinal Edema Based on OCT Image Analysis

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Differential Diagnosis of Retinal Edema Based on OCT Image Analysis

The aim of the work is differential diagnosis of retinal edema, study of deep learning methods and their application to image analysis. The application of convolutional neural networks to the task of semantic segmentation of retinal layers was investigated and its efficiency in selecting two selected layers (pigment epithelium and retina) was proved. A disease classifier based on intelligent analysis of the layers extracted by the neural network was implemented. A proof of its applicability for differential diagnosis of retinal edema was presented. The accuracy of disease prediction amounted to 90%.

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来源期刊

Optical Memory and Neural Networks OPTICS-

CiteScore

1.50

自引率

11.10%

发文量

期刊介绍： The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.