An improved algorithm for detecting pneumonia based on YOLOv3

Abstract

Pneumonia is a disease that develops rapidly and seriously threatens the survival and health of human beings. At present, the computer-aided diagnosis (CAD) of pneumonia is mostly based on binary classification algorithms that cannot provide doctors with location information. To solve this problem, this study proposes an end-to-end highly efficient algorithm for the detection of pneumonia based on a convolutional neural network-Pneumonia Yolo (PYolo). This algorithm is an improved version of the Yolov3 algorithm for X-ray image data of the lungs. Dilated convolution and an attention mechanism are used to improve the detection results of pneumonia lesions. In addition, double K-means is used to generate an anchor box to improve the localization accuracy. The algorithm obtained 46.84 mean average precision (mAP) on the X-ray image dataset provided by the Radiological Society of North America (RSNA), surpassing other detection algorithms. Thus, this study proposes an improved algorithm that can provide doctors with location information on lesions for the detection of pneumonia.