MPFracNet:A Deep Learning Algorithm for Metacarpophalangeal Fracture Detection with Varied Difficulties  

作　　者：Geng Qin Ping Luo Kaiyuan Li Yufeng Sun Shiwei Wang Xiaoting Li Shuang Liu Linyan Xue 

机构地区：[1]College of Quality and Technical Supervision,Hebei University,Baoding,071002,China [2]Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System,Baoding,071002,China [3]National&Local Joint Engineering Research Center of Metrology Instrument and System,Hebei University,Baoding,071002,China

出　　处：《Computers, Materials & Continua》2023年第4期999-1015,共17页计算机、材料和连续体（英文）

基　　金：funded by the Research Fund for Foundation of Hebei University(DXK201914);the President of Hebei University(XZJJ201914);the Post-graduate’s Innovation Fund Project of Hebei University(HBU2022SS003);the Special Project for Cultivating College Students’Scientific and Technological Innovation Ability in Hebei Province(22E50041D).

摘　　要：Due to small size and high occult,metacarpophalangeal fracturediagnosis displays a low accuracy in terms of fracture detection and locationin X-ray images.To efficiently detect metacarpophalangeal fractures on Xrayimages as the second opinion for radiologists,we proposed a novel onestageneural network namedMPFracNet based onRetinaNet.InMPFracNet,a deformable bottleneck block(DBB)was integrated into the bottleneckto better adapt to the geometric variation of the fractures.Furthermore,an integrated feature fusion module(IFFM)was employed to obtain morein-depth semantic and shallow detail features.Specifically,Focal Loss andBalanced L1 Loss were introduced to respectively attenuate the imbalancebetween positive and negative classes and the imbalance between detectionand location tasks.We assessed the proposed model on the test set andachieved an AP of 80.4%for the metacarpophalangeal fracture detection.To estimate the detection performance for fractures with different difficulties,the proposed model was tested on the subsets of metacarpal,phalangeal andtiny fracture test sets and achieved APs of 82.7%,78.5%and 74.9%,respectively.Our proposed framework has state-of-the-art performance for detectingmetacarpophalangeal fractures,which has a strong potential application valuein practical clinical environments.

关 键 词：Deep learning small object detection metacarpophalangeal fractures computer-aided diagnosis(CAD) 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]