  The worldwide spread of pneumonia caused by a novel coronavirus poses an
unprecedented challenge to the world's medical resources and prevention and
control measures. Covid-19 attacks not only the lungs, making it difficult to
breathe and life-threatening, but also the heart, kidneys, brain and other
vital organs of the body, with possible sequela. At present, the detection of
COVID-19 needs to be realized by the reverse transcription-polymerase Chain
Reaction (RT-PCR). However, many countries are in the outbreak period of the
epidemic, and the medical resources are very limited. They cannot provide
sufficient numbers of gene sequence detection, and many patients may not be
isolated and treated in time. Given this situation, we researched the
analytical and diagnostic capabilities of deep learning on chest radiographs
and proposed Cascade-SEMEnet which is cascaded with SEME-ResNet50 and
SEME-DenseNet169. The two cascade networks of Cascade - SEMEnet both adopt
large input sizes and SE-Structure and use MoEx and histogram equalization to
enhance the data. We first used SEME-ResNet50 to screen chest X-ray and
diagnosed three classes: normal, bacterial, and viral pneumonia. Then we used
SEME-DenseNet169 for fine-grained classification of viral pneumonia and
determined if it is caused by COVID-19. To exclude the influence of
non-pathological features on the network, we preprocessed the data with U-Net
during the training of SEME-DenseNet169. The results showed that our network
achieved an accuracy of 85.6\% in determining the type of pneumonia infection
and 97.1\% in the fine-grained classification of COVID-19. We used Grad-CAM to
visualize the judgment based on the model and help doctors understand the chest
radiograph while verifying the effectivene.
