A lightweight convolutional neural network for myositis classification from muscle ultrasound images

现有肌炎超声图像的分类方法存在分类性能差或计算成本高的问题。针对上述问题，本文提出了一种基于软阈值注意力机制的轻量级神经网络。该网络的主干采用深度可分离卷积与常规卷积搭建，通过软阈值注意力机制自适应去除冗余特征，有效捕获关键特征，从而提高分类表现。与目前分类正确率最高的双分支特征融合肌炎分类网络相比，本文提出网络的分类正确率提高了5.9%，达到了96.1%，且其计算量仅为现有方法的0.25%。因此，该网络能以较低的存储与计算成本为医生提供更准确的辅助诊断结果，具有较强的实用价值。.; 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.