# An Improved Algorithm for Detecting Pneumonia Based on YOLOv3
## 1 Introduction  
In recent years, the number of people suffering from pneumonia in the world has increased year by year. In particular, the incidence of pneumonia in infants has increased significantly, which seriously threatens the survival and health of human beings [1]. At present, the essence of most computer-aided diagnosis (CAD) system algorithms for the lungs is image classification. Although this kind of algorithm has the advantages of simple implementation and high accuracy, the output results lack the location information of lesion tissue, so it cannot provide more valuable reference for doctors. The algorithm proposed in this study benefits from the accurate labeling of datasets [2] and the rapid development of a convolutional neural network (CNN)-based objection detection algorithm, which enables it to identify pneumonia and locate pneumonia tissue at the same time, so it can provide more reference information for doctors. The CNN is a kind of artificial neural network with a deep structure and convolution calculation. The CNN has the ability of representation learning, which can capture the spatial local correlation of the input data through convolution operation and obtain the translation invariance of the inputdata through down-sampling operation (see Figure 1). Nowadays, the CNN has become the focus of research in many scientific fields, particularly in the field of computer vision. The advantages of the CNN are that complex pre-processing of images is not required and they can use original images as direct input; thus, they have a wide range of applications. [PERSON] et al. [3] first proposed to train the CNN to extract features using back propagation and used support vector machine (SVM) as a classifier to construct a region-based CNN (RCNN) object detection algorithm. However, the RCNN needs to pre-select regions where objects may exist using selective search methods and separate the extraction feature phase from the classification phase. Such an algorithm is not end-to-end, it is difficult to implement, and it has low computational efficiency. [PERSON] [4] and [PERSON] et al. [5] proposed an end-to-end detection algorithm that integrates the selection of object regions, extraction features, and classification using an extraction feature network and region proposal network (RPN). However, this algorithm is a two-stage detection algorithm that has high hardware requirements and has difficulty achieving real-time detection in the training and testing phases. [PERSON] et al. [6] introduces an end-to-end real-time object detection algorithm called Yolo that uses the CNN to perform the extraction feature, classification, and localization of the object. It is a one-stage detection algorithm that has a high detection speed but unsatisfactory performance on object localization. The Yolov2 algorithm was proposed by [PERSON] and [PERSON] [7]. This algorithm uses K-means clustering to cluster anchor scales as prior knowledge in many datasets to improve object localization. In addition, random scaling has proposed for use to enhance the generalization of the algorithm to different scales. The Yolov3 algorithm, subsequently proposed by [PERSON] and [PERSON] [8], uses Feature Pyramid Net (FPN) [9] to improve performance with respect to missed detection of small objects in Yolov2. The above are the study of CNN-based object detection algorithms. Next, we will learn about the CAD system. At present, most CAD systems for the lung rely on image classification algorithms. This kind of system takes a whole image as input, extracts the feature using a feature extractor, and finally obtains a predictive label of the image from the classifier. This kind of CAD system cannot provide the doctors with accurate location information on lesions; therefore, its usefulness is limited. For example, in a study by [PERSON] et al. [10], the CNN was used to extract features, and an SVM was used as a classifier to detect pneumonia from an input image. In a study by [PERSON] et al.