Artificial intelligence image recognition based on 5G deep learning edge algorithm of Digestive endoscopy on medical construction

In this paper, we use artificial intelligence image recognition to obtain Digestive endoscopy image, and process the image based on 5G Deep learning edge algorithm to judge the disease type of the patient, and then consider the treatment plan. The combination of body area network and edge computing technology can meet the demand of low delay in body area network. In this case, the resource constrained body area network gateway node can process the physiological data collected by it into an offloadable task, and then unload the task and data to the edge computing node according to a certain strategy. The edge computing node completes the corresponding task processing and data storage, and finally provides the results to the relevant medical institutions and body area network users for reading auxiliary diagnosis and treatment of diseases. Studies have shown that 25% of patients with colon polyps have CD4 cells in peripheral blood based on 5G deep learning edge algorithm under artificial intelligence image recognition of Digestive endoscopy. The number of lymphocyte in group of differentiation was less than 200/μL, and the blood RNA in 92.3% patients was lower than 100 IU/ml, while fam CTP (A-cyclic peptide) was lower than 100 IU/ml. Opportunistic infections of the intestine and viruses can directly cause enteropathy because the fluorescence intensity of the probe is essentially unchanged and cannot form a triple helix structure. In terms of feature recognition accuracy, the 5G deep learning edge algorithm in this paper improves accuracy by 68% compared to the simple Yolo algorithm, and is similar in speed. Compared with RCNN algorithm, the accuracy and speed are improved by 21% and 85% respectively. Therefore, the 5G deep learning edge algorithm based on artificial intelligence image recognition has the advantages of accuracy and speed in digestive endoscopy of intelligent medical.