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相关概念视频

Skin Cancer01:30

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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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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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ACO-KELM:抗冠状病毒优化基于内核的Softplus极端学习机器用于皮肤癌的分类.

Nannan Liu1, M R Rejeesh2, Vinu Sundararaj2

  • 1School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo, 315211, China.

Expert systems with applications
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一种新的方法,基于Anti Coronavirus Optimized Kernel的Softplus极端学习机器 (ACO-KSELM),使用高维生物医学数据的特征提取来提高皮肤癌预测的准确性. 这种方法在分类各种皮肤癌类型时达到98%以上的准确性.

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优化抗冠状病毒的优化高维数据集是一个高维数据集.基于内核的软加极端学习机器学习.预测的准确性 预测的准确性皮肤癌是皮肤癌.

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 机器学习 机器学习
  • 皮肤病学 皮肤病学

背景情况:

  • 高维度的生物医学数据集往往含有冗余的特征,阻碍了准确的疾病诊断.
  • 有效的特征提取对于识别潜在数据模式和改善预测模型性能至关重要.

研究的目的:

  • 引入一种新的方法,即基于抗冠状病毒优化内核的Softplus极端学习机器 (ACO-KSELM),用于准确的皮肤癌预测.
  • 解决疾病诊断中大尺寸数据集所带来的挑战.

主要方法:

  • 使用了四个皮肤癌图像数据集 (ISIC 2016,ACS,HAM10000,PAD-UFES-20).这些数据集包括:
  • 应用高斯过器来降低噪音,并使用颜色直方图,Haralick纹理和Hu时刻提取来识别特征.
  • 实施了拟议的ACO-KSELM模型来对皮肤癌类型进行分类.

主要成果:

  • 实现了高预测准确度:98.9% (ISIC 2016),98.7% (ACS),98.6% (HAM10000),以及97.9% (PAD-UFES-20). 通过测试和测试,我们可以获得高的预测准确度.
  • 成功地将提取的特征分类为基底细胞癌 (BCC),状细胞癌 (SCC),动动性角质瘤 (ACK),斑块性角质瘤 (SEK),恩病 (BOD),黑色素瘤 (MEL) 和Nevus (NEV).

结论:

  • ACO-KSELM方法显示出从高维皮肤镜像中准确诊断皮肤癌的巨大潜力.
  • 提出的特征提取和分类方法有效地处理复杂的生物医学数据,以提高诊断准确度.