[Nondestructive detection of total nitrogen content in leaves of Santalum album based on ST-PCA-BP neural network.]

Efficacy of liver cancer microwave ablation through ultrasonic image guidance under deep migration feature algorithm

Objective:

To explore the therapeutic effects of ultrasound-guided microwave ablation and radio frequency ablation for liver cancer patients.

Methods:

Seventy-eight patients with microwave ablation were rolled into the experimental group and 56 patients with radio frequency ablation were in the control group. This study was conducted from March 1, 2019 to June 30, 2020 in our hospital. Based on Convolutional Neural Networks (CNN) and Migration feature (MF), a new ultrasound image diagnosis algorithm CNNMF was constructed, which was compared with AdaBoost and PCA-BP based on Principal component analysis (PCA) and back propagation (BP), and the accuracy (Acc), specificity (Spe), sensitivity (Sen), and F1 values of the three algorithms were calculated. Then, the CNNMF algorithm was applied to the ultrasonic image diagnosis of the two patients, and the postoperative ablation points, complications and ablation time were recorded.

Results:

The Acc (96.31%), Spe (89.07%), Sen (91.26%), and F1 value (0.79%) of the CNNMF algorithm were obviously larger than the AdaBoost and the PCA-BP algorithms (P< 0.05); in contrast with the control group. The number of ablation points in the experimental group was obviously larger, and the ablation time was obviously shorter (P<0.05); the experimental group had one case of liver abscess and two cases of wound pain after surgery, which were both obviously less than the control group (four cases; five cases) (P<0.05)

Conclusion:

In contrast with traditional algorithms, the CNNMF algorithm has better diagnostic performance for liver cancer ultrasound images. In contrast with radio frequency ablation, microwave ablation has better ablation effects for liver cancer tumors, and can reduce the incidence of postoperative complications in patients, which is safe and feasible.

Prediction of Mumps Incidence Trend in China Based on Difference Grey Model and Artificial Neural Network Learning

Background:

We aimed to compare the prediction efficiency of back propagation (BP) network and grey model (GM) (1.1) for mumps infectious diseases and compare the application effect of the two models.

Methods:

By calculating the average incidence rate of mumps in January 2014 –2016, we conducted the modeling of the BP time series, GM (1,1) grey model and the combination models of them, and predicted the incidence rate in June 2016 in comparison with the actual one. We compared the quarterly incidence rate to test the two prediction models, and compared the advantages and disadvantages of these models.

Results:

R value of BP model was 68.45%, for GM (1,1) was 58.49%, and for combined forecasting model was 86.95%. We used the principal component analysis clustering method to control the samples, and found that the samples were close to the population mean. We found that the GM (1.1) model was more suitable for the prediction of mumps infection mode. We carried out dimension reduction analysis on the model data, and the accuracy of the data after dimension reduction is within the range of Da. For the discrete degree of the data in the combined model, matlab pipeline was used to verify the reliability of the data and results. By calculation after manifold optimization small error probability was P=0.875 and semi mean relative error 2.43%.

Conclusion:

BP, GM (1,1) is a better method for modeling the epidemic trend of mumps in China, but the efficiency of prediction is not as high as the combination of them.