An Improved Algorithm for Detecting Pneumonia Based on YOLOv3

Applied Sciences

Shangjie Yao¹  Shuhao Ma²  Yaowu Chen³  Xiang Tian⁴  Rongxin Jiang⁵ 

[1] Institute of Advanced Digital Technology and Instrumentation, Zhejiang University, Zhejiang 310027, China;The Institute of Information Science and Technology Instrumentation, Dalian Maritime University, Dalian 116026, China;The State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China;Zhejiang Provincial Key Laboratory for Network Multimedia Technologies, Hangzhou 310027, China;Zhejiang University Embedded System Engineering Research Center, Ministry of Education of China, Hangzhou 310027 China;

关键词: convolutional neural network;    pneumonia detection;    medical image;   

DOI  :  10.3390/app10051818

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown