Abstract
With the improvement of people's requirements for the accuracy of disease detection, increasing researchers apply image recognition to the detection and diagnosis of gastrointestinal diseases. In addition, the evaluation of detection and diagnostic accuracy is also an important factor in the diagnosis and treatment of gastrointestinal diseases. Referring to the QoS and QoE evaluation scheme in 5G network communication, this paper proposes a secondary quality experience evaluation model for gastrointestinal diseases. Firstly, three mandatory detection implementation nodes of 5G network communication are created in the image recognition model, and the first node detects the detected points (images of intestinal diseases) in 5G network communication in the form of interactive information. Then, take the above detection results as the first level to obtain the quality detection strategy results, and the second node uses the policy detection server to evaluate the measurement results as the results of the second level quality evaluation strategy. Finally, the third node is used to evaluate the results of the secondary quality evaluation, and the evaluation results of gastrointestinal disease detection are returned to the output of the whole model. In this paper, 200 CT images of the gastrointestinal tract in the CT colonography dataset were used to evaluate the performance of the model. The study shows that after 200 gastrointestinal CT images are imported into the model, 173 samples have a score of more than 85% for the output of other nodes at the end of the enforcement node, accounting for 86.5% of the sample size. After 24 samples were evaluated by the second level, the output result was the first level scoring error, accounting for 12% of the sample size. Three samples were judged as abnormal images by the first level enforcement node, accounting for 1.5% of the sample size. From the results, the secondary quality experience evaluation model can improve the image recognition accuracy of gastrointestinal disease detection and diagnosis from 86.5% to 98.5%, which greatly improves the accuracy of image recognition.
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WZ: Experiment designing, editing. YG: Experiment process implementing, writing. SS: Data collecting. FG: Data analysis.
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Zhang, W., Gao, Y., Song, S. et al. Image recognition for gastrointestinal disease detection and diagnosis in QoS and QoE evaluation of 5G network communication. Soft Comput 26, 13799–13813 (2022). https://doi.org/10.1007/s00500-022-07368-2
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DOI: https://doi.org/10.1007/s00500-022-07368-2