Design And Implementation Of Lung Nodule Aided Detection System Based On Convolutional Neural Network

Worldwide,lung diseases have seriously threatened people's lives,learning and work.To solve this problem,the most effective way to improve the survival rate of lung cancer patients is to achieve early detection,accurate diagnosis and precise treatment.A significant feature of lung diseases is the production of solitary pulmonary nodules.Therefore,how to detect solitary pulmonary nodules becomes an important research direction.In 2016,the China Health Planning Commission and epidemiologists found that the number of cases of lung diseases admitted by hospitals is increasing year by year.China is currently in a period of high incidence of lung diseases.With the continuous development of CT technology,the diagnostic efficiency of doctors has also been greatly improved.However,the traditional CT data has a large amount of information,requiring specialist doctors to spend a lot of time reading,but there are a lot of difficult-to-find lesions under a huge amount of data.If the doctor can not accurately determine these tiny lesions,the patient's diagnosis results Can not be guaranteed.Therefore,the use of computer-assisted physicians for diagnosis and diagnosis can greatly reduce the workload of doctors to diagnose and help doctors to obtain accurate diagnosis results.This article takes conventional medical imaging as the research object,explores a method of accurately detecting pulmonary nodules using computer aided methods,and implements a pulmonary nodule auxiliary detection system based on previous experiments.In this process,by reading relevant literature on the analysis and detection of lung nodules,the detection methods and research status of different nodules were understood,and the existing methods were compared and analyzed,and a lung nodule based on CNN was proposed.The detection method is based on this method,and a pulmonary nodule auxiliary detection system is designed and developed.The system implements four basic functions,including reading and displaying CT information,initial lung segmentation,extraction of ROI regions,and display of nodule regions.