X-ray images play an important role in the diagnosis of pneumonia disease， but they are susceptible to noise pollution during imaging， resulting in the imaging features of pneumonia being inconspicuous and an insufficient extraction of lesion features. A dense dual-stream focused network DDSF-Net is proposed in this paper for the development of an aided diagnosis model for pneumonia to address the abovementioned problems. The main steps of this method are as follows. First， a residual multi-scale block is designed， a multi-scale strategy is used to improve the adaptability of the network to different sizes of pneumonia lesions in medical images， and a residual connection is used to improve the efficiency of the network parameter transfer. Secondly， a dual-stream dense block is designed， a dense unit with a parallel structure for the global information stream and the local information stream is used， whereby the transformer learns global contextual semantic information. The convolutional layer performs local feature extraction， and a deep and shallow feature fusion of the two information streams is achieved using a dense connection. Finally， focus blocks with central attention operation and neighborhood interpolation operation are designed， background noise information is filtered by cropping the medical image size， and detailed features of lesions are enhanced by interpolating the medical images with magnification. In comparison with typical models used for a pneumonia X-ray dataset， the model introduced in this paper obtained better performance with a 98.12% accuracy， 98.83% precision， 99.29% recall， 98.71% F1， 97.71% AUC and 15729 s training time. Compared with DenseNet， ACC and AUC were improved by 4.89% and 4.69%， respectively. DDSF-Net effectively alleviates the problems of inconspicuous pneumonia imaging features and insufficient extraction of lesion features. The validity of this model and robustness of this paper are further verified by a heat map and three public datasets.