计算机视觉辅助青光眼诊断算法研究OA
Research on Computer Vision-assisted Glaucoma Diagnosis Algorithm
青光眼是一类视神经损伤疾病,青光眼筛查工作对眼科医师的诊断水平有很高的要求.为帮助医生更好地观察青光眼病理特征,提高医生的主观诊断效率和准确率,本文应用计算机视觉辅助诊断算法增强视网膜图像,并使用神经网络构建图像的分类识别模型.实验结果显示,所构建的神经网络对视网膜图像分类的准确率为 92.05%.
Glaucoma is a type of optic nerve injury disease,and the screening of glaucoma requires high diagnostic skills from ophthalmologists.To help doctors better observe the pathological characteristics of glaucoma and improve their subjective diagnostic efficiency and accuracy,this paper applies computer vision assisted diagnostic algorithms to enhance retinal images and constructs a classification recognition model for images using neural networks.The experimental results show that the accuracy of the constructed neural network in retinal image classification is 92.05%.
张蓝天;管军霖
桂林电子科技大学计算机与信息安全学院 广西 桂林 541004
计算机与自动化
计算机视觉青光眼辅助诊断深度学习
Computer VisionGlaucomaAssisted DiagnosisDeep Learning
《福建电脑》 2024 (007)
29-32 / 4
本文得到国家级大学生创新创业训练计划项目(No.202310595011)、广西可信重点实验室项目(No.kx202208)、广西高校中青年教师科研基础能力提升(No.2023KY0213)资助.
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