Screening Abnormal Chest X-Ray in Children Based on DenseNet ECA
Chest X-ray is the most common and easily available low-cost imaging method for screening pulmonary abnormalities in children.However,in some areas where medical resources are scarce,due to the small number of experienced radiologists,the interpretation of chest X-ray is inefficient,which is easy to cause missed diag-nosis and misdiagnosis of children with pulmonary abnormalities.Therefore,this paper took children's health and ab-normal chest X-ray as the research object,improved DenseNet by using ECA attention mechanism and PReLU activa-tion function,and proposed a deep learning modelDenseNet ECA for children's abnormal chest X-ray screening task.The experimental results show that the classification effect of this model for children's healthy and abnormal chest X-rayis better than that of common convolutional neural networkmodels.The classification accuracy,sensitivity and spe-cificity can reach 93.57%,91.47%and 95.83%respectively,and the parameter quantity is only 6.96M.The model can help doctors to pre-screen abnormalchest X-ray of children,effectively reduce the pressure ofclinical film reading and improve the diagnostic efficiency of doctors.
Abnormal chest x-ray screening in childrenAttention mechanismSimulation
NETL
NSTL
万方数据
