Pneumonia image segmentation method based on improved FCN
Aiming at the problem that the recognition of pneumonia lesions in chest X-ray images is heavy workload and the results are not accurate enough,this study proposed an image segmentation method of pneumonia lesions based on improved FCN.First,data sets of healthy lung images and infected pneumonia images in Pascal dataset format were constructed.Secondly,the convergence rate of training loss between different ResNet networks and traditional VGG networks is compared.Then ResNet50 network with the best effect was used to replace the original VGG network in the classic FCN algorithm as the backbone network,and a multi-scale feature extraction module was proposed.Finally,the improved FCN network is compared with the traditional FCN network,LR-ASPP,DEEPLAB-V3.The experimental results show that the improved FCN network can accurately segment pneumonia lesions of various shapes and sizes in chest X-ray,and the segmentation effect is good,which can provide a reliable basis for clinical diagnosis of pneumonia.
NETL
NSTL
万方数据
