Csec-net:一种新的深度特征融合和受控制的火虫特征选择框架,用于白血病分类.
Sarmad Maqsood1,2, Robertas Damaševičius1, Rytis Maskeliūnas1
1Centre of Real Time Computer Systems, Faculty of Informatics, Kaunas University of Technology, LT-51386 Kaunas, Lithuania.
Health information science and systems
|December 31, 2024
概括
这项研究引入了一种用于计算机辅助白血病诊断的新型深度学习方法,在分类血细胞图像方面实现了高精度. 该方法提高了这种危及生命的癌症的诊断效率.
科学领域:
- 医疗成像医学成像
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 目前,白血病的诊断依赖于手动显微镜图像分析.
- 机器学习,特别是深度学习,为图像分类提供了先进的解决方案.
- 需要针对白血病的自动化,准确的诊断工具.
研究的目的:
- 开发和评估用于计算机辅助白血病诊断的深度学习模型.
- 从血液细胞图像中提高白血病检测的准确性和效率.
- 建立一个强大的自动化系统来分类白血病亚型.
主要方法:
- 在白血病数据集图像的预处理.
- 通过使用五个预训练的卷积神经网络模型 (MobileNetV2,EfficientNetB0,ConvNeXt-V2,EfficientNetV2,DarkNet-19) 转移学习.
- 通过卷积稀疏图像分解进行深度特征的融合,然后进行控制的火虫特征选择和多类支向量机器分类.
主要成果:
- 拟议的深度学习算法在四个数据集中实现了高精度:ALLID_B1 (99.64%),ALLID_B2 (98.96%),C_NMC 2019 (96.67%) 和ASH (98.89%).
- 该方法在白血病图像分类方面与现有方法相比,表现优越.
- 成功应用于15562张图像,证实了其稳定性和可扩展性.
结论:
- 开发的深度学习框架为计算机辅助白血病诊断提供了有效和准确的方法.
- 这种方法有可能显著帮助临床医生在早期和精确检测白血病.
- 这项研究强调了深度学习在推进癌症诊断方面的力量.
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