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相关概念视频

Diabetic Retinopathy01:27

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...
Diabetic Nephropathy01:28

Diabetic Nephropathy

Definition Diabetic nephropathy is a chronic kidney complication that results from prolonged hyperglycemia.Prevalence It is the most common cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) worldwide, affecting up to half of individuals with diabetes.Pathophysiology • Sustained hyperglycemia triggers multiple hemodynamic and metabolic changes in the kidney. • Early in the disease, increased renal blood flow and glomerular hyperfiltration occur due to afferent arteriolar...

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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深度学习增强的糖尿病视网膜病变图像分类

Ghadah Alwakid1, Walaa Gouda2, Mamoona Humayun3

  • 1Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakakah, Saudi Arabia.

Digital health
|August 17, 2023
PubMed
概括
此摘要是机器生成的。

一个深度学习模型准确地识别了糖尿病视网膜病变 (DR) 的阶段. 增强图像数据改善了模型性能,这对于防止DR不可逆转的视力损失至关重要.

关键词:
亚普托斯 (APTOS) 是一个在DDR中,DDR是指DDR.糖尿病视网膜病变 - 糖尿病视网膜病变增强 增强 增强 增强深度学习是一种深度学习.密集网-12121是一个密集网.增强图像增强图像的图像.转移学习转移学习视力损失 视力损失

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科学领域:

  • 眼科医生 眼科 眼科
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 糖尿病视网膜病变 (DR) 是可预防的失明的主要原因.
  • 早期发现和治疗DR对于保护视力至关重要.
  • 深度学习 (DL) 为自动化DR选提供了潜力.

研究的目的:

  • 开发和评估一个DL模型,准确分类糖尿病视网膜病变的五个阶段.
  • 评估图像增强技术对DR检测DL模型性能的影响.

主要方法:

  • 为DR阶段识别实施了DenseNet-121深度学习模型.
  • 两个数据集,APTOS和DDR,用于模型培训和验证.
  • 图像增强技术用于提高数据集质量和平衡.

主要成果:

  • 拟议的DL模型在两个数据集上都实现了高精度:APTOS上的测试精度为98.36%,DDR上的测试精度为79.67%.
  • 在DDR数据集上,Top-2和Top-3准确度分数分别超过92%和98%.
  • 精度,回忆和F1分数指标证实了该模型在DR阶段分类中的有效性.

结论:

  • 深度学习模型可以有效地识别糖尿病视网膜病变的多个阶段.
  • 使用更高质量的增强图像可以显著提高DL模型的性能.
  • 这种方法有望提高DR查效率和准确性.