关键词: 胸部疾病分类, 胸部X光片, 注意力机制, 卷积神经网络, 特征融合

Abstract: The automatic identification of thoracic diseases from chest X-rays is a significant area of research in computer-aided diagnosis. However, many existing methods for thoracic disease classification struggled to handle differences in lesion area sizes and often failed to accurately identify and localize the lesion areas. To address the above problems, a Cross-scale Attention Network (CANet) approach for thoracic disease classification was proposed. In this method, DenseNet-121 was employed as the feature extraction network, and three main modules were integrated: Self Aware Attention (SAA), Upward Focus Attention (UFA) and Downward Guidance Attention (DGA) modules. The SAA module was designed to refine spatial pathological features by extracting channel and region information relevant to thoracic diseases, while reducing interference from irrelevant areas. In order to achieve cross-scale interaction of spatial context information, image feature calibration was performed using the upward focus attention and downward guidance attention modules. Additionally, the Spatial Attention Pyramid Pooling (SAPP) module was proposed to fuse multi-scale features from different feature maps, thereby enhancing the detection performance for thoracic diseases. Experiments on the proposed method using the ChestX-ray14 and DR-Pneumonia datasets achieve Area Under Curve (AUC) values of 83.4% and 82.6%, respectively, outperforming the DualCheXNet, A3Net, and CheXGAT methods. Compared with the CheXGAT method, the AUC values of the proposed method improve by 0.7 and 0.1 percentage points, respectively. The experimental results show that the proposed method effectively identifies critical information in chest radiography, significantly improving the performance of thoracic disease classification.

Key words: thoracic disease classification, chest X-ray, attention mechanism, convolutional neural network, feature fusion