Research On Lung Cancer Recognition Algorithm Based On Improved Deep Learning For Medical Images

With the development of medical intelligence,computer technology and artificial intelligence technology,computed tomography(CT)technology is widely used for the initial examination of patients in the process of diagnosis and treatment of pulmonary lesions.The number of lung slice images obtained by CT scanning is huge,and the long-term lung slice images inspection work is a great burden for radiologists,and it leads to a decline in the accuracy of manual diagnosis.Using deep learning technology to assist doctors in identifying diseased areas in the lungs can greatly reduce the burden on doctors and improve the efficiency of patient diagnosis.Lung cancer has become the malignant tumor with the highest morbidity and mortality in China and even the world,which seriously threatens human health.In the process of lung cancer diagnosis and treatment,a large amount of lung cancer big data has been generated.To solve the problem that the current lung cancer-assisted diagnosis method based on deep learning cannot accurately locate the lesion,this paper proposes a lung cancer identification method based on improved U-Net.The method first uses U-Net to obtain the precise location of the lesion,and then uses a Convolutional Neural Network(CNN)to classify whether it is cancerous,and completes the identification of cancerous lesions.The experimental results show that this method can locate lung lesions more accurately.The Dice Similarity Coefficient(DSC)of the segmentation effect exceeds 80%,and the accuracy of classification diagnosis of lung cancer lesions reaches 90.7%.Lung cancer is generally considered to be terminally ill.Existing medical technology can give patients supportive treatment,but generally cannot completely cure the cancer.In this case,the doctor will often inform the patient of the expected survival time,and can use the expected survival time to formulate a targeted treatment plan for the patient.There are many factors that affect the survival time of lung cancer patients.In this paper,an improved random forest survival prediction algorithm is proposed to analyze and judge lung cancer data.The random forest classification algorithm and the multi-layer perceptron classification algorithm are combined to realize the prediction of patient survival time.The algorithm models and analyzes the patient’s physical characteristics,cancer staging status and life characteristics,and predicts the expected survival time of lung cancer patients.The experimental results show that the prediction accuracy of the prediction algorithm exceeds 70%,which is conducive to improving the treatment effect of lung cancer Improve the survival chance of cancer patients.