深度神经网络可以在红外高光谱图像上区分甲状腺病理
Matheus de Freitas Oliveira Baffa1, Denise Maria Zezell2, Luciano Bachmann3
1Department of Computing and Mathematics, University of São Paulo, Ribeirão Preto, SP, Brazil.
Computer methods and programs in biomedicine
|March 5, 2024
概括
红外高光谱成像与深度学习相结合,准确地分类甲状腺组织,将健康细胞与癌症和病理区分开来. 这一进步有助于提高诊断效率,从而改善患者的预后.
科学领域:
- 生物医学光学 生物医学光学
- 数字病理学数字病理学
- 机器学习在医疗保健中的应用
背景情况:
- 甲状腺病理如甲状腺炎,甲状腺功能低下症和癌症显著影响健康.
- 组织学分析对于诊断甲状腺疾病至关重要,但目前的方法面临局限性.
- 超光谱成像为基于分子光相互作用的生物样本分析提供了一种新的方法.
研究的目的:
- 开发一种方法,从组织学甲状腺样本中获取红外吸收光谱.
- 创建一个深度学习模型,根据光谱图案对甲状腺组织进行分类.
- 为了区分健康,癌症和的甲状腺组织.
主要方法:
- 从组织学甲状腺样本的每个声点获取红外吸收光谱.
- 开发一个多类完全连接的神经网络模型.
- 使用k倍交叉验证用于模型性能评估.
主要成果:
- 在分类甲状腺组织方面达到93.66%的平均准确性.
- 显示高灵敏度 (93.47%) 和特异性 (96.93%).
- 证实了使用红外高光谱成像用于甲状腺病理特征的可行性.
结论:
- 红外高光谱成像通过吸收度测量有效地表征健康的甲状腺组织和病理.
- 拟议的深度学习模型显示了提高诊断效率的潜力.
- 这项技术可以在甲状腺疾病管理中改善患者的治疗结果.
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