颜色融合对深度学习的效果 脑膜黑色素瘤的分类
Albert K Dadzie1, Sabrina P Iddir2, Mansour Abtahi1
1Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL, 60607, USA.
Eye (London, England)
|May 21, 2024
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 (UM) 和胸腔瘤至关重要.
- 区分这些情况可以防止良性瘤的过度治疗,并确保对恶性瘤的及时干预.
- 超广场视网膜成像在眼科诊断中越来越多地被使用.
研究的目的:
- 验证深度学习算法的有效性,用于分类UM和胸腔内.
- 评估不同颜色融合技术对这些算法的诊断性能的影响.
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个冠状腺神经).
- 卷积神经网络 (CNN) 用于图像分类.
- 使用F1分数,准确度和ROC AUC评估分类性能,比较早期,中期和晚期的色彩融合策略.
主要成果:
- 深度学习的性能受到色彩融合方法的显著影响.
- 单色分析显示,与绿色或蓝色相比,红色通道图像的性能优于绿色或蓝色.
- 在多色分析中,中间色融合表现出比早期或晚期融合更好的结果.
结论:
- 深度学习提供了一个可行的工具,用于自动分类阴道黑色素瘤和胸腔瘤.
- 选择颜色融合技术是优化眼科成像深度学习模型性能的关键因素.
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