Pneumonia is a common lung disease that is more prone to affect the elderly and those with weaker respiratory systems.However,hospital medical resources are limited,and sometimes the workload of physicians is too high...Pneumonia is a common lung disease that is more prone to affect the elderly and those with weaker respiratory systems.However,hospital medical resources are limited,and sometimes the workload of physicians is too high,which can affect their judgment.Therefore,a good medical assistance system is of great significance for improving the quality of medical care.This study proposed an integrated system by combining transfer learning and gradient-weighted class activation mapping(Grad-CAM).Pneumonia is a common lung disease that is generally diagnosed using X-rays.However,in areaswith limited medical resources,a shortage of medical personnel may result in delayed diagnosis and treatment during the critical period.Additionally,overworked physicians may make diagnostic errors.Therefore,having an X-ray pneumonia diagnosis assistance system is a significant tool for improving the quality of medical care.The result indicates that the best results were obtained by a ResNet50 pretrained model combined with a fully connected classification layer.A retraining procedure was designed to improve accuracy by using gradient-weighted class activation mapping(Grad-CAM),which detects the misclassified images and adds weights to them.In the evaluation tests,the final combined model is named Grad-CAM Based Pneumonia Network(GCPNet)out performed its counterparts in terms of accuracy,precision,and F1 score and reached 97.2%accuracy.An integrated system is proposed to increase model performance where Grad-CAM and transfer learning are combined.Grad-CAM is used to generate the heatmap,which shows the region that the model is focusing on.The outcomes of this research can aid in diagnosing pneumonia symptoms,as themodel can accurately classify chest X-ray images,and the heatmap can assist doctors in observing the crucial areas.展开更多
基金supported by the National Science and Technology Council,Taiwan,under Grants NSTC 111-2218-E-194-007,NSTC 112-2218-E-194-006,MOST 111-2823-8-194-002,MOST 111-2221-E-194-052,MOST 109-2221-E-194-053-MY3,NSTC 112-2221-E-194-032supported by the Advanced Institute of Manufacturing with High-Tech Innovations (AIM-HI)from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE)in Taiwan.
文摘Pneumonia is a common lung disease that is more prone to affect the elderly and those with weaker respiratory systems.However,hospital medical resources are limited,and sometimes the workload of physicians is too high,which can affect their judgment.Therefore,a good medical assistance system is of great significance for improving the quality of medical care.This study proposed an integrated system by combining transfer learning and gradient-weighted class activation mapping(Grad-CAM).Pneumonia is a common lung disease that is generally diagnosed using X-rays.However,in areaswith limited medical resources,a shortage of medical personnel may result in delayed diagnosis and treatment during the critical period.Additionally,overworked physicians may make diagnostic errors.Therefore,having an X-ray pneumonia diagnosis assistance system is a significant tool for improving the quality of medical care.The result indicates that the best results were obtained by a ResNet50 pretrained model combined with a fully connected classification layer.A retraining procedure was designed to improve accuracy by using gradient-weighted class activation mapping(Grad-CAM),which detects the misclassified images and adds weights to them.In the evaluation tests,the final combined model is named Grad-CAM Based Pneumonia Network(GCPNet)out performed its counterparts in terms of accuracy,precision,and F1 score and reached 97.2%accuracy.An integrated system is proposed to increase model performance where Grad-CAM and transfer learning are combined.Grad-CAM is used to generate the heatmap,which shows the region that the model is focusing on.The outcomes of this research can aid in diagnosing pneumonia symptoms,as themodel can accurately classify chest X-ray images,and the heatmap can assist doctors in observing the crucial areas.
