【摘 要】
：
慢性肾脏病是一种在世界范围内广泛存在的肾脏疾病。该疾病一旦发展到晚期,伴随而来的是严重并发症与较高死亡风险。因此,早期筛查对于慢性肾脏病诊治至关重要。超声作为一种无创方法,能动态观察肾脏形态和病理特征,常用于肾脏检查。本文提出一种新的卷积神经网络模型,称为纹理分支网络,基于超声影像作慢性肾脏病筛查。该模型通过在经典卷积神经网络中引入纹理分支来提取和优化纹理特征,可自动生成输入图像的纹理特征和深度特
【出 处】
：
Frontiers of Information Technology & Electronic Engineering
论文部分内容阅读
慢性肾脏病是一种在世界范围内广泛存在的肾脏疾病。该疾病一旦发展到晚期,伴随而来的是严重并发症与较高死亡风险。因此,早期筛查对于慢性肾脏病诊治至关重要。超声作为一种无创方法,能动态观察肾脏形态和病理特征,常用于肾脏检查。本文提出一种新的卷积神经网络模型,称为纹理分支网络,基于超声影像作慢性肾脏病筛查。该模型通过在经典卷积神经网络中引入纹理分支来提取和优化纹理特征,可自动生成输入图像的纹理特征和深度特征,并使用融合信息进行分类。此外,通过迁移学习训练网络的主干部分,并在具有226张超声影像的数据集上开展
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