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Pneumonia, an infectious disease that can influence the lungs, is a severe medical field topic. Therefore, how to correctly classify images of pneumonia is very important. The limitations of traditional machine learning algorithms and the significant improvement of computing performance make deep learning widely used. At present, using a convolutional neural network to classify pneumonia is still the mainstream method. This paper provides a modified capsule network to detect and classify pneumonia by using X-ray pictures. The model consists of two parts: encoder and decoder. Encoder contains convolutional layer, primary capsule layer, and digital capsule layer. The primary capsule layer and digital capsule layer convert a scalar into a vector and then try to cluster vectors of the same category by dynamic routing. The decoder contains a deconvolutional layer. The image is reconstructed by up-sampling the vector generated by the encoder, and the reconstructed image is compared with the original image to make the features extracted by the encoder more representative. The training and testing process takes place on the dataset "Labeled Optical Coherence Tomography (OCT) and Chest XRay Images for Classification." This dataset contains a total of 5856 pictures. We divide the images into the training set and testing set at a ratio of 8:2. The accuracy rate on this dataset is 98.6%. This model has a more straightforward structure and fewer parameters than other popular models, which means that it can be more easily deployed in various conditions in practical applications.
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