[用于肌肉超声图像肌炎分类的轻量级卷积神经网络]。

Q4 Medicine
Hao Tan, Xun Lang, Tao Wang, Bingbing He, Zhiyao Li, Yu Lu, Yufeng Zhang
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引用次数: 0

摘要

现有的肌炎超声图像分类方法存在分类性能差或计算成本高的问题。针对这一难题,我们提出了一种基于软阈值关注机制的轻量级神经网络,以满足更好的肌炎分类需求。该网络是通过交替使用深度可分离卷积(DSC)和传统卷积(CConv)构建的。此外,还利用软阈值关注机制来增强关键特征的提取能力。与目前分类准确率最高的双分支特征融合肌炎分类网络相比,本文提出的网络的分类准确率提高了 5.9%,达到 96.1%,其计算复杂度仅为现有方法的 0.25%。这些结果证明,本文提出的方法能以较低的计算成本为医生提供更准确的分类结果,从而极大地帮助医生进行临床诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
[A lightweight convolutional neural network for myositis classification from muscle ultrasound images].

Existing classification methods for myositis ultrasound images have problems of poor classification performance or high computational cost. Motivated by this difficulty, a lightweight neural network based on a soft threshold attention mechanism is proposed to cater for a better IIMs classification. The proposed network was constructed by alternately using depthwise separable convolution (DSC) and conventional convolution (CConv). Moreover, a soft threshold attention mechanism was leveraged to enhance the extraction capabilities of key features. Compared with the current dual-branch feature fusion myositis classification network with the highest classification accuracy, the classification accuracy of the network proposed in this paper increased by 5.9%, reaching 96.1%, and its computational complexity was only 0.25% of the existing method. The obtained results support that the proposed method can provide physicians with more accurate classification results at a lower computational cost, thereby greatly assisting them in their clinical diagnosis.

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来源期刊
生物医学工程学杂志
生物医学工程学杂志 Medicine-Medicine (all)
CiteScore
0.80
自引率
0.00%
发文量
4868
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