肺炎检测在医学图像处理中具有重要的研究意义,针对当前经典检测算法对肺炎病灶检测精度较低的问题,本文提出一种基于多尺度卷积神经网络的X光图像中肺炎病灶检测算法。在基础特征提取网络中加入特征通道注意力模块,突出特征图中含有大量肺炎病灶信息的特征通道,抑制不含病灶信息或者含有大量无用信息的特征通道,形成高质量特征图;然后通过统计分析,使用聚类算法设计了一系列不同宽高比以及缩放尺度的候选框以适用于肺炎病灶检测。同时,在含有肺炎病灶的胸部X光图像数据集上进行了单模型和多模型检测实验,其中单模型下检测精度为82.52%,多模型融合下检测精度为89.08%。通过对比实验与结果分析,验证了本文算法在检测精度方面优于当前其他检测算法,适用于X光图像中肺炎病灶检测。

Pneumonia detection has important research significance in medical image processing. For the problem that the current classical detection algorithms has low accuracy in detecting pneumonia lesions. This paper presents an algorithm for detecting pneumonia lesions in X-ray images based on multi-scale convolutional neural networks. The feature channel attention module is added to the basic feature extraction network to highlight the channel containing useful information in the feature map, and to suppress the feature channel without lesion information or containing a large amount of useless information to form a high-quality feature map. Then through statistical analysis, a series of candidate frames with different aspect ratios and scaling scales are designed using clustering algorithm to be suitable for pneumonia lesion detection. In this paper, the single-model and multi-model detection experiments are performed on chest X-ray datasets containing pneumonia lesions. The detection accuracy is 82.52% in the case of single model and 89.08% in the case of multi-model fusion. Through comparison experiments and results analysis, the proposed algorithm is superior to other detection algorithms in pneumonia lesion detection and is suitable for pneumonia lesion detection in X-ray images.