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相关概念视频

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Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
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For most patients, experiencing several weeks of polyuria, polydipsia, fatigue, and significant weight loss may indicate the presence of diabetes. Furthermore, adults displaying the phenotypic appearance of type 2 diabetes (particularly those who are obese and not initially insulin-requiring), may have islet cell autoantibodies, suggesting autoimmune-mediated β cell destruction and a diagnosis of latent autoimmune diabetes of adults (LADA). The categorization of glucose homeostasis is...
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High Resolution 3D Imaging of the Human Pancreas Neuro-insular Network
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使用人类面部皮肤区域测量1型和2型糖尿病.

L Aneesh Euprazia1, A Rajeswari1, K K Thyagharajan2

  • 1Computer Science and Engineering, Velammal Engineering College, Chennai, India.

Journal of diabetes research
|October 5, 2023
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概括
此摘要是机器生成的。

视觉糖尿病学分析面部皮肤以检测糖尿病 (DM). 一个新的多波切换卷积神经网络 (MWTCNN) 实现了98.3%的准确性,为持续的葡萄糖监测提供了一种非侵入性方法.

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科学领域:

  • 眼科和医学成像学
  • 医疗保健中的人工智能
  • 内分泌学 在内分泌学.

背景情况:

  • 糖尿病 (DM) 具有高血糖水平的特征,导致因血液流量改变而影响面部皮肤的并发症.
  • 与DM相关的面部皮肤变化,包括贝尔,面部红斑症,硬质皮质症和白风,存在诊断挑战.
  • 目前DM的诊断方法通常涉及诸如血清葡萄糖检测等侵入性程序.

研究的目的:

  • 开发和验证一种使用面部皮肤成像分析糖尿病 (DM) 的非侵入性方法.
  • 提出一种新的深度学习模型,用于从面部图像中准确地测量糖尿病.
  • 通过视觉糖尿病学来实现持续的葡萄糖监测.

主要方法:

  • 收集了印度糖尿病患者的200张面部图像,包括那些患有特定皮肤疾病的人.
  • 利用Viola Jones算法进行面部检测和HSV颜色模型细分,以识别受影响的皮肤区域.
  • 开发并应用了多波形变换卷积神经网络 (MWTCNN) 用于特征提取和糖尿病测量.

主要成果:

  • 与现有的深度学习模型相比,拟议的MWTCNN模型表现出优异的性能.
  • 在使用面部图像检测糖尿病时,达到98.3%的高诊断准确率.
  • 成功地从缩放的面部图像中提取了相关特征,用于糖尿病患者的测量.

结论:

  • 面部皮肤区域分析提供了一种有希望的非侵入性方法来检测和监测糖尿病.
  • MWTCNN模型为视觉糖尿病学提供了一个高度准确和高效的工具.
  • 这种方法消除了血清刺痛的需要,促进了持续的葡萄糖监测.