用高光谱成像进行甲状腺组织分类的深度学习架构的比较分析
Matheus de Freitas Oliveira Baffa1, Denise Maria Zezell2, Luciano Bachmann1
1São Paulo State University, Ribeirão Preto, Brazil.
Scientific reports
|August 26, 2025
概括
这项研究将微福里埃变换红外光谱 (微FTIR) 与甲状腺组织分析的深度学习相结合. 一维卷积神经网络 (1D-CNN) 在分类,癌症和健康的甲状腺组织方面表现出卓越的准确性.
科学领域:
- 医疗诊断
- 生物分子分析
- 光谱学
背景情况:
- 超光谱成像 (HSI) 通过分析组织中的生物分子差异化的光谱信息,为医学诊断提供了潜力.
- 分析高维度的HSI数据存在重大挑战.
- 深度学习,包括循环神经网络 (RNN) 和卷积神经网络 (CNN),对于复杂的医学数据分析至关重要.
研究的目的:
- 引入一种新的方法,将微福里埃变换红外光谱 (微FTIR) 与深度学习相结合,用于甲状腺组织的分类.
- 在基于区域的甲状腺组织分类中比较RNN,完全卷积神经网络 (FCNN) 和1D-CNN的性能.
- 评估这些深度学习模型在识别,癌症和健康甲状腺组织类型方面的精度和准确性.
主要方法:
- 开发和评估了三个深度学习架构:RNN,FCNN和1D-CNN.
- 使用微FTIR光谱法从甲状腺组织样本中获取光谱数据.
- 使用了60名患者的数据集,并使用分组的10倍交叉验证评估模型进行了可靠的性能评估.
主要成果:
- 在分类甲状腺组织光谱数据方面,1D-CNN模型达到最高准确率97.60%.
- RNN和FCNN模型的准确度分别为96.88%和93.66%.
- 该研究表明1D-CNN在精确的基于区域的组织分类中表现优异.
结论:
- 微FTIR光谱和深度学习的整合,特别是1D-CNN,显著提高了甲状腺病理分析的精度.
- 这种方法为准确区分各种甲状腺组织疾病提供了强大的工具.
- 这些发现强调了深度学习在通过光谱数据分析推进医学诊断方面的潜力.
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