颜色融合对深度学习的影响 乌维色素瘤的分类
Xincheng Yao1, Albert Dadzie1, Sabrina Iddir
1University of Illinois Chicago.
Research square
|November 21, 2023
概括
深度学习准确地分类了脑膜黑色素瘤和胸腔瘤. 深度学习模型中的中间色彩融合显著改善了这些眼睛疾病的分类性能.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 在患者管理中,精确区分脑膜黑色素瘤和胸腔瘤至关重要.
- 区分这些疾病可以防止对良性病变进行不必要的干预,并确保对恶性病例及时治疗.
研究的目的:
- 为了验证深度学习 (DL) 用于分类脑膜黑色素瘤 (UM) 和胸腔瘤.
- 评估各种颜色融合技术对DL分类性能的影响.
主要方法:
- 对798张超广场视网膜图像的回顾性分析,来自438名患者 (157个UM,281个nevus).
- 卷积神经网络 (CNN) 模型被用来使用不同的色彩融合策略 (早期,中期,晚期) 来分类图像.
- 使用特异性,敏感性,F1得分,准确性和ROC AUC来评估性能;突出性地图可视化了分类驱动器.
主要成果:
- 颜色融合显著影响了DL的性能.
- 红色通道图像在单色分析中显示出优异的性能.
- 在多色分析中,中间聚变的性能优于早期和晚期聚变方法.
结论:
- 深度学习为自动UM和胆道神经分类提供了一种可行的方法.
- 优化色彩融合技术对于提高DL诊断工具的准确性至关重要.
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