Zhou Mengran, Gao Lipeng, Bian Kai, Wang Haonan, Wang Ning, Chen Yue, Liu Siyi
School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China.
School of Mechanics and Optoelectronic Physics, Anhui University of Science and Technology, Huainan, 232001, Anhui, China.
Sci Rep. 2025 Aug 10;15(1):29243. doi: 10.1038/s41598-025-13479-1.
Pulmonary disease can severely impair respiratory function and be life-threatening. Accurately recognizing pulmonary diseases in chest X-ray images is challenging due to overlapping body structures and the complex anatomy of the chest. We propose an adaptive multiscale feature fusion model for recognizing Chest X-ray images of pneumonia, tuberculosis, and COVID-19, which are common pulmonary diseases. We introduce an Adaptive Multiscale Fusion Network (AMFNet) for pulmonary disease classification in chest X-ray images. AMFNet consists of a lightweight Multiscale Fusion Network (MFNet) and ResNet50 as the secondary feature extraction network. MFNet employs Fusion Blocks with self-calibrated convolution (SCConv) and Attention Feature Fusion (AFF) to capture multiscale semantic features, and integrates a custom activation function, MFReLU, which is employed to reduce the model's memory access time. A fusion module adaptively combines features from both networks. Experimental results show that AMFNet achieves 97.48% accuracy and an F1 score of 0.9781 on public datasets, outperforming models like ResNet50, DenseNet121, ConvNeXt-Tiny, and Vision Transformer while using fewer parameters.
肺部疾病会严重损害呼吸功能并危及生命。由于身体结构重叠和胸部解剖结构复杂，在胸部X光图像中准确识别肺部疾病具有挑战性。我们提出了一种自适应多尺度特征融合模型，用于识别肺炎、肺结核和新冠肺炎等常见肺部疾病的胸部X光图像。我们引入了一种自适应多尺度融合网络（AMFNet）用于胸部X光图像中的肺部疾病分类。AMFNet由一个轻量级的多尺度融合网络（MFNet）和作为二级特征提取网络的ResNet50组成。MFNet采用带有自校准卷积（SCConv）和注意力特征融合（AFF）的融合块来捕获多尺度语义特征，并集成了一个自定义激活函数MFReLU，用于减少模型的内存访问时间。一个融合模块自适应地组合来自两个网络的特征。实验结果表明，AMFNet在公共数据集上达到了97.48%的准确率和0.9781的F1分数，在使用更少参数的情况下优于ResNet50、DenseNet121、ConvNeXt-Tiny和视觉Transformer等模型。
