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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.
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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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可解释的基于人工智能的皮肤癌检测使用CNN,粒子群优化和机器学习.

Syed Adil Hussain Shah1,2, Syed Taimoor Hussain Shah2, Roa'a Khaled3

  • 1Department of Research and Development (R&D), GPI SpA, 38123 Trento, Italy.

Journal of imaging
|December 27, 2024
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概括

这项研究引入了一种高效的人工智能 (AI) 管道,用于准确检测皮肤癌. 人工智能模型显著提高了诊断的准确性和可解释性,帮助临床医生在早期检测.

关键词:
剥离 剥离 剥离 剥离可解释的人工智能特性提取 特性提取功能选择 功能选择中等高斯 SVM.粒子群集优化 粒子群集优化亚空间 KNNN 亚空间 KNN转移学习转移学习

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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:

  • 皮肤癌是一个全球性的健康问题,需要改进的诊断方法.
  • 传统的视觉皮肤癌诊断是主观的,耗时的.
  • 目前用于皮肤癌检测的AI方法在效率和解释性方面存在局限性.

研究的目的:

  • 为自动化皮肤癌诊断开发一个全面和高效的AI管道.
  • 为了提高人工智能驱动的皮肤癌检测的准确性和可解释性.
  • 解决当前人工智能方法的计算和解释性挑战.

主要方法:

  • 使用预训练的卷积神经网络 (CNN) 模型进行转移学习,选择Xception作为最佳.
  • 实现了粒子群集优化,将特征的维度从1024减少到508.
  • 集成的机器学习分类器 (Subspace KNN,中高斯 SVM) 和可解释的AI (XAI) 技术 (Grad-CAM,LIME,封闭敏感性).

主要成果:

  • 在ISIC 2018数据集上达到98.5%,在HAM10000数据集上达到86.1%的高诊断准确度.
  • 通过减少特征维度,显著提高了计算效率.
  • 使用XAI技术增强模型解释性,为诊断决策提供了洞察力.

结论:

  • 拟议的AI管道为自动化皮肤癌诊断提供了强大,高效和可解释的解决方案.
  • 这种方法有可能显著帮助临床医生在早期和准确的皮肤癌检测.
  • 转移学习,特征选择和XAI的整合代表了皮肤学AI的重大进步.