Research On Semantic Annotation Method For Clinical Diagnosis Of Pulmonary Nodules Based On CT Images

Lung cancer is the most frequent cancer in the world in recent decades.The related research results show that early cure and early treatment can improve the cure rate of lung cancer patients.With the continuous development of science and technology,the computer aided diagnosis system can handle the pulmonary nodules properly,which can provide strong support for the diagnosis of pulmonary nodules by doctors.At the same time,the CAD system reduces the workload of doctors,improves work efficiency,avoids the inaccuracy of the doctors' subjectivity and differences,and helps to improve the efficiency and accuracy of the diagnosis.Therefore,using the computer and its related equipment and technology to assist the doctors to extract the suspected area of the lung,extract the related features of the suspicious area,and finally carry out a series of detection so that the focus area can be identified,and the semantic annotation is carried out.This is very important for researchers and clinicians.But at present,there is a great difference between the information extracted by doctors and the information easily understood by doctors,and the accuracy of information extraction is not high.1.In this paper,an auxiliary diagnostic system based on semantic annotation is proposed.The semantic information obtained is in accordance with the information of the doctor's diagnosis.The main work is to collect data from the LIDC database,extract the information of the expert tagging,and save the ACCESS database,and establish a processing system.Extract the grayscale shape and texture features,and use the classifier to build a supervised semantic annotation model.2.Combining the characteristics of data and the similarity between feature selection and clustering,a supervised feature selection algorithm based on split clustering is proposed.The feature subset after feature selection continues to establish the annotation model.In this experiment,the accuracy rate of the semantic annotation model of lung nodules established by the extraction of the underlying features and the classifier learning and training was 100%,and the other four semantic annotation accuracy rates were improved compared with those of previous studies.By using the proposed clustering based feature selection algorithm,the number of features used by the five semantic models is greatly reduced,and the accuracy of the tagging is improved.