Research on Sports Health Care Information System Based on Computer Deep Learning Algorithm - Ma, Xiaojun; Zhang, Zhenfeng (Computational intelligence and neuroscience, 2022)
There are various problems in diagnosing and treating tumor diseases in significant hospitals. The content includes misjudgement and over-surgery issues. For example, the judgment of pulmonary nodules mainly relies on artificial experience, and most of the artificial experience is too radical. This paper is mainly based on the extensive medical data of significant hospitals, extracts the diagnosis and treatment data and digital images of similar cases from the extensive database, classifies them through the deep learning of the computer, and then proposes the control mechanism and the solution of the doctor’s misjudgement and excessive medical treatment. This method mainly relies on the CT and MRI digital images of various types of tumor diseases accumulated in the history of major hospitals. Based on the preliminary judgment of each diagnosis and treatment and the results of surgical and pathological examinations, the accumulation of various types of digital images from the history is used. The features are analysed and extracted, the model is built, and finally, a predictive analysis system for this type of tumor is obtained, which can predict the benign and malignant cases of currently occurring cases and avoid the limitations and instability of artificial experience greatest extent. It is proved by experiments and combined with Spearman to remove redundancy. The redundancy removal method SVM_RFE is used for dimensionality reduction. The method can timely correct the misjudgement of the doctor’s experience and effectively reduce the instability of the manual, which provides a solution for solving the contradiction between doctors and patients and improving the scientific of diagnosis and treatment.