Image Features of Magnetic Resonance Imaging under the Deep Learning Algorithm in the Diagnosis and Nursing of Malignant Tumors

Clinical Significance of Action Research-Based Seamless Care to Improve Imaging Efficiency and Patients' Cognition, and Alleviate Patient Anxiety

Objective

The present study was undertaken to assess the clinical significance of action research-based seamless care to improve imaging efficiency and alleviate patient anxiety.

Methods

A total of eighty patients who underwent imaging examinations in our hospital between May 2019 and November 2020 were recruited for this study. The patients were randomly assigned to two groups: the control group receiving routine care and the observation group receiving seamless care based on action research. The random assignment was conducted using a simple random sampling technique, ensuring an equal allocation of participants to each group at a 1:1 ratio, resulting in 40 cases in each group. Outcome measures included imaging examination duration, mean nursing duration, examination cognition, and negative emotion scores.

Results

Seamless care provided shorter imaging examination duration and nursing duration, and better ensured uneventful examinations than routine care (P<0.05). Patients given seamless care exhibited higher examination cognition versus those receiving routine care (P<0.05). Seamless care offered more mitigation of negative emotions for patients than routine care (P<0.05).

Conclusion

Action research-based seamless care effectively improves imaging efficiency and patients' awareness of imaging examinations and contributes to alleviating patients' adverse events.

Diagnosis System of Microscopic Hyperspectral Image of Hepatobiliary Tumors Based on Convolutional Neural Network

Hepatobiliary tumor is one of the common tumors and cancers in medicine, which seriously affects people's lives, so how to accurately diagnose it is a very serious problem. This article mainly studies a diagnostic method of microscopic images of liver and gallbladder tumors. Under this research direction, this article proposes to use convolutional neural network to learn and use hyperspectral images to diagnose it. It is found that the addition of the convolutional neural network can greatly improve the actual map classification and the accuracy of the map, and effectively improve the success rate of the treatment. At the same time, the article designs related experiments to compare its feature extraction performance and classification situation. The experimental results in this article show that the improved diagnostic method based on convolutional neural network has an accuracy rate of 85%–90%, which is as high as 6%–8% compared with the traditional accuracy rate, and thus it effectively improves the clinical problem of hepatobiliary tumor treatment.