Pneumonia is a disease that serious threat to human health, timely and accurate detection of pneumonia can help patients receive treatment as soon as possible. Therefore, in this paper, an improved Multi branch YOLO detection algorithm based on YOLOv3 is proposed. The output features of multi branch dilation convolution are used to replace the features of different levels in YOLOv3 for detection. Boosting thought is introduced into multi branch convolutional neural network, and the network is optimized with maximum entropy approach. Each convolution branch is regarded as a weak classifier, and the maximum entropy approach is adopted to promote each branch to learn the similar detection ability, so as to avoid the degeneration of multi branch convolution model into single-branch convolution model. Experimental data are provided by the radiological society of North America with lung X-ray images. The results show that algorithm's detection accuracy on experimental data sets is higher than other target detection algorithms.