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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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使用焦点调制网络进行皮肤损伤分类.

Hasan Zan1

  • 1Department of Computer Engineering, Mardin Artuklu University, Mardin, Turkey.

Annals of the New York Academy of Sciences
|November 3, 2025
PubMed
概括
此摘要是机器生成的。

焦点调制网络 (FMN) 提供了一种准确而有效的方法,用于从皮肤镜像中分类皮肤病变. 这种方法可以提高皮肤癌的早期诊断,提高对公共数据集的可解释性和性能.

关键词:
深度学习是一种深度学习.焦点调制网络的焦点调制网络.皮肤癌检测 皮肤癌检测皮肤病变的分类 皮肤病变的分类

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科学领域:

  • 皮肤病学 皮肤病学
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 自动皮肤病变分类对于早期皮肤癌检测至关重要.
  • 挑战包括视觉相似性,病变变异性和图像工件.
  • 当前的深度学习模型通常是计算密集型,缺乏可解释性.

研究的目的:

  • 提出和评估使用焦点调制网络 (FMN) 进行皮肤病变分类的框架.
  • 解决现有模型在处理高分辨率医疗图像和临床整合方面的局限性.

主要方法:

  • 开发了一个使用四种焦模网络 (FMN) 的框架:Tiny,Small,Base和Large.
  • 在三个公共数据集上评估了FMN框架:ISIC 2017,ISIC 2018和ISIC 2019.
  • 评估了分类准确性,计算效率和模型可解释性.

主要成果:

  • 实现了高分类准确度:在ISIC 2019上达到97.8%,在ISIC 2018上达到96.4%,在ISIC 2017上达到88.1%.
  • 结果与以前的研究结果相匹配或超过.
  • 通过调制器可视化证明了模型的可解释性.

结论:

  • FMN为自动皮肤病变分类提供了准确,高效和透明的解决方案.
  • 由于其性能和可解释性,拟议的方法适合临床整合.
  • FMN有效地捕捉了本地和全球特征,在高分辨率的医疗图像上表现优于变压器.