融合局部与全局特征的肺炎医学影像分类

针对现有肺炎医学影像识别研究在浅层网络忽略全局特征导致特征提取不全且模型规模较大的问题, 提出了一种基于CNN和注意力机制的轻量化模型提高肺炎类型的识别效率. 采用轻量化模型结构减少模型参数量, 通过增大卷积核, 引入高效通道注意力和自注意力机制解决网络重要信息丢失和无法提取底层全局信息的问题, 通过双分支并行提取局部和全局信息并使用多尺度通道注意力提高二者融合质量, 使用CLAHE算法优化原始数据. 实验结果表明, 该模型在保证轻量性的同时准确率、灵敏度、特异性较原模型分别提高2.59%, 3.1%, 1.38%, 并优于当前优秀的其他分类模型, 具有更强的实用性.

Pneumonia Medical Image Classification Based on Fusion of Local and Global Features

In view of the problem of incomplete feature extraction and large model size caused by ignoring global features in shallow networks in existing pneumonia medical image recognition research, a lightweight model based on convolutional neural network (CNN) and attention mechanism is proposed to improve the recognition efficiency of pneumonia types. A lightweight model structure is used to reduce the number of model parameters. By increasing the convolution kernel, efficient channel attention and self-attention mechanisms are introduced to solve the problem of loss of important network information and the inability to extract underlying global information. Local and global information is extracted in parallel through dual branches, and multi-scale channel attention is utilized to improve the fusion quality of the two. The CLAHE algorithm is employed to optimize the original data. The experimental results show that the accuracy, sensitivity, and specificity of the model are increased by 2.59%, 3.1%, and 1.38% respectively compared with those of the original model while ensuring lightness, and the proposed model outperforms other current excellent classification models and has stronger practicability.