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相关概念视频

Classification of Systems-I01:26

Classification of Systems-I

215
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Classification of Systems-II01:31

Classification of Systems-II

177
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
177
Aggregates Classification01:29

Aggregates Classification

345
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
345
Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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相关实验视频

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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使用混合深度学习和殖民地优化进行光学一致性断层图像分类.

Awais Khan1, Kuntha Pin1, Ahsan Aziz1

  • 1Department of ICT Convergence, Soonchunhyang University, Asan 31538, Republic of Korea.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种深度学习方法,用于使用光学连贯断层扫描 (OCT) 图像准确检测视网膜疾病. 自动化系统实现了99.1%的准确性,大大改善了手动诊断.

关键词:
与年龄相关的黄斑变性.殖民地优化殖民地优化分支视网膜静脉闭塞 视网膜静脉闭塞中央视网膜静脉封闭中央血清胆色素变异症 (Central Serous Chorioretinopathy) 是一种血清胆色素变异症.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.糖尿病黄斑胀 糖尿病黄斑胀功能选择 功能选择机器学习是机器学习.光学连贯性断层扫描技术

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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:

  • 从光学一致性断层扫描 (OCT) 图像中手动检测视网膜疾病是主观的,容易出错.
  • 现有的自动化方法需要进一步提高准确性,以便可靠的早期检测.
  • 深度学习方法显示出增强自动视网膜疾病诊断的潜力.

研究的目的:

  • 开发和评估一种基于深度学习的自动化方法,用于使用OCT图像检测和分类多种视网膜疾病.
  • 提高视网膜疾病自动诊断的准确性和可靠性.
  • 为了比较拟议的方法的性能与没有特征优化技术.

主要方法:

  • 使用了三种预训练的深度学习模型 (DenseNet-201,InceptionV3,ResNet-50) 通过转移学习进行特征提取.
  • 使用殖民地优化 (ACO) 来增强提取的功能并选择最相关的功能.
  • 使用K-近邻 (KNN) 和支持矢量机 (SVM) 算法与优化功能的视网膜疾病分类.

主要成果:

  • 拟议的深度学习方法在结合群优化 (ACO) 时实现了99.1%的高精度.
  • 没有ACO,该方法的准确性达到97.4%.
  • 该系统展示了最先进的性能,在视网膜疾病分类的准确性方面超过了现有的技术.

结论:

  • 开发的深度学习模型有效地检测和分类来自OCT图像的视网膜疾病.
  • 殖民地优化显著提高了自动诊断系统的性能.
  • 这种自动化方法为手动诊断提供了可靠和准确的替代方案,有助于早期检测糖尿病黄斑胀和与年龄相关的黄斑变性等疾病.