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相关概念视频

Skin Cancer01:30

Skin Cancer

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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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Deep learning based gasket fault detection: a CNN approach.

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使用图像处理技术检测和分类黑色素瘤皮肤癌.

Chandran Kaushik Viknesh1, Palanisamy Nirmal Kumar1, Ramasamy Seetharaman1

  • 1Department of Electronics and Communication Engineering, College of Engineering Guindy Campus, Anna University, Chennai 600025, India.

Diagnostics (Basel, Switzerland)
|November 14, 2023
PubMed
概括

这项研究引入了使用深度学习进行早期黑色素瘤诊断的计算机辅助检测. 卷积神经网络实现了91%的准确性,超越了支持矢量机器,现在在网络和移动应用中.

关键词:
达戈 (Django) 是一个多元化的语言.卷积神经网络是一种卷积神经网络.黑色素瘤是一种黑色素瘤.皮肤癌是皮肤癌.支持矢量机器的支持矢量机器

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科学领域:

  • 皮肤病学 皮肤病学
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 人类皮肤癌,特别是黑色素瘤,死亡率很高.
  • 早期发现可显著改善治疗结果.
  • 传统的活检方法用于黑色素瘤诊断是侵入性的和耗时的.

研究的目的:

  • 通过图像分析开发和评估计算机辅助检测 (CAD) 技术,用于早期黑色素瘤诊断.
  • 为了比较卷积神经网络 (CNN) 和支持矢量机器 (SVM) 在皮肤癌分类中的性能.
  • 将最准确的模型部署到可访问的网络和移动应用程序中.

主要方法:

  • 研究了两个主要方法:CNN (AlexNet,LeNet,VGG-16) 和使用RBF内核的SVM.
  • 图像处理技术用于提取特征进行分类.
  • 准确度最高的CNN模型使用Django和Android Studio集成到Web和移动应用程序中.

主要成果:

  • 在100个时代之后,CNN模型实现了91%的分类准确度.
  • SVM分类器的准确率达到了86.6%.
  • 该研究探讨了模型深度和数据集大小对CNN性能的影响.

结论:

  • 与SVM相比,CNN为早期黑色素瘤检测提供了一个高度准确和高效的方法.
  • 开发的CAD系统,集成到网络和移动平台,提高了早期皮肤癌诊断的可访问性.
  • 这项研究支持人工智能在医学诊断中的进步,以改善患者的治疗结果.