Medical detection technology-from biochemical analysis to medical imaging has witnessed tremendous progress in the past decades,driven by the breakthrough of biomedical and scientific theories,the innovation of engine...Medical detection technology-from biochemical analysis to medical imaging has witnessed tremendous progress in the past decades,driven by the breakthrough of biomedical and scientific theories,the innovation of engineering techniques,as well as the discovery of new contrast mechanisms.These substantial progresses have significantly promoted展开更多
Pneumoconiosis is a disease characterized by pulmonary tissue deposition caused by dust exposure in the workplace.In China,due to the large number and wide distribution of pneumoconiosis patients,there is a high deman...Pneumoconiosis is a disease characterized by pulmonary tissue deposition caused by dust exposure in the workplace.In China,due to the large number and wide distribution of pneumoconiosis patients,there is a high demand for the case data of lung biopsy during the diagnosis of pneumoconiosis.This text studied the application of medical image detection technology in pneumoconiosis diagnosis based on deep learning(DL).A medical image detection and convolution neural network(CNN)based on DL was analyzed,and the application of DL medical image technology in pneumoconiosis diagnosis was researched.The experimental results in this paper showed that in the last round of testing,the accuracy of ResNet model including deconvolution structure reached 95.2%.The area under curve(AUC)value of the working characteristics of the subject is 0.987.The sensitivity was 99.66%,and the specificity was 88.61%.The non staging diagnosis of pneumoconiosis improved the diagnostic sensitivity while ensuring high specificity.At the same time,Delong test method was used to conduct AUC analysis on the three models,and the results showed that model C was more effective than model A and model B.There is no significant difference between model A and model B,and there is no significant difference in diagnostic efficiency.In a word,the diagnosis of the model has high sensitivity and low probability of missed diagnosis,which can greatly reduce the working pressure of diagnostic doctors and effectively improve the efficiency of diagnosis.展开更多
文摘Medical detection technology-from biochemical analysis to medical imaging has witnessed tremendous progress in the past decades,driven by the breakthrough of biomedical and scientific theories,the innovation of engineering techniques,as well as the discovery of new contrast mechanisms.These substantial progresses have significantly promoted
文摘Pneumoconiosis is a disease characterized by pulmonary tissue deposition caused by dust exposure in the workplace.In China,due to the large number and wide distribution of pneumoconiosis patients,there is a high demand for the case data of lung biopsy during the diagnosis of pneumoconiosis.This text studied the application of medical image detection technology in pneumoconiosis diagnosis based on deep learning(DL).A medical image detection and convolution neural network(CNN)based on DL was analyzed,and the application of DL medical image technology in pneumoconiosis diagnosis was researched.The experimental results in this paper showed that in the last round of testing,the accuracy of ResNet model including deconvolution structure reached 95.2%.The area under curve(AUC)value of the working characteristics of the subject is 0.987.The sensitivity was 99.66%,and the specificity was 88.61%.The non staging diagnosis of pneumoconiosis improved the diagnostic sensitivity while ensuring high specificity.At the same time,Delong test method was used to conduct AUC analysis on the three models,and the results showed that model C was more effective than model A and model B.There is no significant difference between model A and model B,and there is no significant difference in diagnostic efficiency.In a word,the diagnosis of the model has high sensitivity and low probability of missed diagnosis,which can greatly reduce the working pressure of diagnostic doctors and effectively improve the efficiency of diagnosis.
