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基于ACGANAN的小样本的AML白细胞分类方法

Chenxuan Zhang¹, Junlin Zhu²

¹School of Artificial Intelligence, 232838 Chongqing University of Technology , Chongqing, PR.China.

Biomedizinische Technik. Biomedical engineering

|March 28, 2024

概括

辅助分类生成对抗网络 (ACGAN) 有效地分类小白细胞样本. 这种深度学习方法显示出高精度,超过了白血病诊断的其他方法.

科学领域:

血液学血液学血液学
医疗成像医学成像
人工智能的人工智能

背景情况:

急性髓性白血病 (AML) 的诊断依赖于对外围血液涂抹的显微镜分析.
准确识别和计数白细胞对于诊断血性恶性瘤如AML至关重要.
血液细胞识别的深度学习 (DL) 方法需要大量的数据集,对小样本大小构成挑战.

研究的目的:

用有限的数据评估辅助分类生成对抗网络 (ACGAN) 对白细胞分类的有效性.
在小样本场景中,将ACGAN的性能与传统分类器和当前最先进的方法进行比较.

主要方法:

在ACGAN的培训中,ACGAN使用了TCIA的白细胞分类数据集.
使用准确度,精度,回忆和F1分数来评估性能.
结果与两个经典分类器和先进的深度学习技术进行了比较.

主要成果:

在验证组中,ACGAN 实现了 97.1% 的准确性.
对ACGAN的精度,回忆和F1分数分别为97.5%,97.3%和97.4%.
与其他先进方法相比,ACGAN表现出优越或具有竞争力的分类准确性.

结论:

ACGAN是一种可行且具有竞争力的深度学习方法,用于分类小白细胞样本数据集.
该方法显示了提高AML查和诊断的准确性和效率的巨大潜力.
ACGAN解决了在将深度学习应用于医疗图像分析时经常遇到的数据限制.

关键词:

在ACGANAN中,我们可以看到ACGAN 数据增强数据增强图像的分类图像的分类.植物小规模数据集

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