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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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Detection and Isolation of Circulating Melanoma Cells using Photoacoustic Flowmetry
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使用CNN技术开发一种有效的黑色素瘤检测方法.

Devika Moturi1, Ravi Kishan Surapaneni2, Venkata Sai Geethika Avanigadda2

  • 1Department of Computer Science and Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India. devikamoturi@gmail.com.

Journal of the Egyptian National Cancer Institute
|February 26, 2024
PubMed
概括

这项研究强调了深度学习在皮肤癌检测方面的有效性. 一个定制的卷积神经网络 (CNN) 实现了95%的准确性,超过了MobileNetV2,现在通过Web应用程序可用.

关键词:
定制的CNN定制的CNN深度学习是一种深度学习.弗拉斯克玻璃瓶是什么意思在HAM1000000中,我们可以看到HAM10000的价格.黑色素瘤是一种黑色素瘤.移动网络V2 移动网络V2皮肤癌检测 皮肤癌检测

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科学领域:

  • 在瘤学瘤学.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 皮肤癌是一种普遍且可能致命的疾病,全球发病率正在上升.
  • 黑色素瘤是一种主要的亚型,在临床上具有侵略性,并导致大多数皮肤癌死亡.
  • 通过查及早检测对于有效的皮肤癌管理至关重要.

研究的目的:

  • 评估用于准确和快速检测皮肤癌的深度学习技术.
  • 为了比较MobileNetV2和定制的卷积神经网络 (CNN) 的性能,用于分类恶性和良性皮肤瘤.
  • 开发一个用户友好的网络应用程序,用于皮肤病变图像分析.

主要方法:

  • 使用了HAM10000数据集,包括10,000张皮肤病变图像.
  • 应用深度学习模型,特别是MobileNetV2和定制的CNN,用于瘤分类.
  • 将实施的深度学习技术的诊断性能进行比较.

主要成果:

  • 定制的CNN模型实现了95%的诊断准确度,超过了MobileNetV2的85%.
  • 使用Python框架开发了一个Web应用程序,具有图形用户界面 (GUI).
  • 图形用户界面使用户能够输入患者详细信息,上传病变图像,并接收恶性瘤和受影响百分比的预测.

结论:

  • 与MobileNetV2.2相比,定制的CNN在黑色素瘤检测方面表现出更高的准确性.
  • 开发的网络应用程序为初步皮肤癌评估提供了一个可访问的工具.