The traditional deep models for classifying common pneumonia and novel coronavirus pneumonia are mainly explored through the following aspects. First, the deep model can be improved by increasing the generalization ability to obtain more comprehensive COVID-19 features. Second, Through pre-treatment and post-treatment, the COVID-19 features obtained by depth architecture are fine-tuned. Finally, Exploring a special loss function consistent with COVID-19 features is useful for fitting the speed of the deep model. To effectively solve the above challenges, this paper proposes an ultrasound COVID-19 classification based on the novel module-based dual-path network (MD-DPNet). The proposed model takes advantage of the fact that most pathological features are concentrated in relatively small regions and the COVID-19 artifact localization is obtained through modules. Through training the regular modular dataset, the proposed MD-DPNet divides the complete task into multiple low-coupling sub-ta...

With a large-scale novel coronavirus pneumonia (COVID-19) outbreak, more and more researchers have acquired convenient and efficient COVID-19 infection status through medical imaging. Here, due to the excellent features of zero-radiation and rapid clinical examination, ultrasound images have been used to assist doctors in COVID-19 diagnosis. To effectively identify the pathological differences between common pneumonia and novel coronavirus pneumonia, an ultrasound COVID-19 classification based on the novel module-based dual-path network (MD-DPNet) is proposed. Specifically, this article effectively improves the generalization ability by adding the progressive heatmaps intuitively representing the lesion density with the original ultrasound images. Meanwhile, the proposed algorithm creates regular modular sets to reduce the calculating loads and the coupling between each module, which takes advantage of the fact that most pathological features are concentrated in relatively small region...