皮肤网-INIO:使用融合辅助深度神经网络和改进的自然灵感优化算法进行多类皮肤损伤定位和分类
Muneezah Hussain1, Muhammad Attique Khan2,3, Robertas Damaševičius4
1Department of CS, HITEC University, Taxila 47080, Pakistan.
Diagnostics (Basel, Switzerland)
|September 28, 2023
概括
这项研究引入了一个自动化的深度学习框架,用于改进皮肤病变的分类. 这种新的方法增强了对比度,并优化了特征选择,在从皮肤显微镜图像分类皮肤病变方面实现了高准确性.
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:
- 深层卷积神经网络 (DCNNs) 在皮肤病变分类方面表现有前途,但面临着有限的数据和类变化等挑战.
- 准确的分类受到类间相似性,类内变异和难以关注关键损伤区域的阻碍.
研究的目的:
- 开发一种自动化的深度学习框架,用于使用皮肤显微镜图像进行多类皮肤病变分类.
- 为了增强对比度,优化超参数,融合深度特征,并选择最佳特征以提高分类准确度.
主要方法:
- 一种新的预处理技术,使用暗通道雾和上下过来增强对比度.
- 通过遗传算法 (GA) 微调三个预训练的DCNN模型,并使用转移学习和超参数优化.
- 特性提取,使用基于串行相关的方法进行融合,并使用改进的反Lion优化算法进行选择.
主要成果:
- 该框架实现了高精度,在ISIC2018数据集上达到96.1%,在ISIC2019数据集上达到99.9%.
- 与现有的最先进的技术相比,拟议的方法显示出更高的准确性.
- 自动化的超参数选择和特征选择/融合过程增强了学习和性能.
结论:
- 该框架有效地提高了癌症区域的对比度,提高了诊断能力.
- 超参数和特征选择的自动化技术优化了学习过程和分类准确性.
- 拟议的融合和选择方法保持高精度,同时减少计算时间.
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