Abstract

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Title:: Detecting foreign objects in chest radiographs based on a deep learning method

Author(s):: HOU Pengfei1; 4; SHEN Xiuming2; YUAN Mingyuan3; SUN Jiuai4; 1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 2. Healthcare Professional Training Center of Songjiang District, Shanghai 201600, China 3. Department of Radiology, Zhoupu Hospital Affiliated to Shanghai University of Medicine and Health Sciences, Shanghai 201318, China 4. School of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China

Keywords:: Keywords: chest radiograph foreign object detection deep learning YOLO network

Abstract:: Abstract: In order to improve the ability of automatically detecting foreign objects in chest radiographs, a deep learning network is used to capture the imaging features of foreign objects with various scales and shapes efficiently, thereby realizing the automatic and stable detection of various foreign objects in chest radiographs. During network construction, according to the imaging features of foreign objects, YOLO v4 network is improved by embedding SE-block (Squeeze and Excitation) into the feature extraction network CSPDarkNet53 to make the model capable of distinguishing and utilizing the information of each channel. Experimental results demonstrate that the proposed deep learning network achieves 92% accuracy and 83% recall rate for foreign object detection. Therefore, the proposed deep learning approach can be used to identify foreign objects in chest radiographs and assess image quality automatically and objectively, which lays a foundation for the quality control of radiographic images.