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通过使用多个内核学习框架和超声图像进行肝囊性球菌多重分类

Zhengye Wang¹, Miao Wu², Qian Liu³

¹Center for Disease Control and Prevention, Xinjiang Production and Construction Corps, Urumqi, China; Ultrasound Department, State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Disease in Central Asia, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.

Ultrasound in medicine & biology

|April 28, 2024

概括

准确诊断肝发性囊性赤道球菌病 (HCE) 是至关重要的. 多核学习支持矢量机 (MK-SVM) 在将HCE超声波图像分类为子类型时获得了96.6%的准确性.

科学领域:

医学成像医学成像
机器学习机器学习
寄生病是一种寄生病.

背景情况:

正确诊断肝发性囊性球菌 (HCE) 对于有效治疗至关重要.
超声波成像是HCE诊断的主要工具.
将HCE分类为亚型有助于治疗规划.

研究的目的:

评估计算机辅助诊断 (CAD) 技术的诊断准确性.
使用CAD将HCE超声波图像分为五种不同的亚型.
评估一种新的多核学习方法的性能.

主要方法:

分析了来自967名患者的1820张HCE超声波图像的数据集.
开发了一种多核学习 (MK-SVM) 方法来提取纹理和深度特征.
将MK-SVM方法与决策树,随机森林和梯度增强决策树算法进行了比较.
使用五倍交叉验证来验证性能.

主要成果:

在使用融合特征集时,MK-SVM实现了最高的分类准确率96.6%.
开发的MK-SVM方法超过了其他评估的机器学习算法.
纹理和深度特征被MK-SVM有效地学习和利用.

结论:

多核学习方法证明了HCE分类的巨大潜力.
MK-SVM有效地整合了各种图像功能,以提高诊断准确度.
这种CAD技术为HCE亚型分类提供了有价值的应用.

关键词:

深度特征是深度特征的特征.肝发性 cystic echinococcosis 肝发性 cystic echinococcosis 肝发性 cystic echinococcosis 肝发性 cystic echinococcosis 肝发性 cystic echinococcosis 肝发性 cystic echinococcosis 肝发性 cystic echinococcosis 肝发性 cystic echinococcosis 肝发性 cystic echinococcosis 肝发性 cystic echinococcosis 肝发性 cystic echinococcosis 肝发性 cystic echinococcosis 肝发性 cystic echinococcosis 肝发性 cystic 多重分类是指多重分类.多个内核学习多个内核学习质地特征质地特征超声波图像中的超声波图像.

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