Search

In the field of tumor diagnosis, the AI-assisted diagnosis system can accurately distinguish tumor attributes and malignant tumor stages, thereby prolonging the survival time of patients. Taking breast tumor as an example, this paper proposes an artificial intelligence-assisted diagnosis model based on supervised learning to solve the problem of overfitting caused by the large amount of data in the feature extraction process. When extracting features, effective feature dimensionality reduction is achieved by introducing hierarchical clustering analysis, and the classified feature data is used as the feature input of the artificial neural network model, so as to realize the effective training of the classifier. The experimental results show that the accuracy and AUC value of the proposed algorithm are improved compared with the control algorithm, indicating that the model can not only solve the problem of overfitting caused by the description of massive feature regions, but also enhance the generalization of the artificial intelligence-aided diagnosis system. The ability to achieve high-precision differentiation of mammography-targeted breast tumors. [ABSTRACT FROM AUTHOR]

The well inclination and azimuth are the main measurement parameters in the calculation of the wellbore trajectory. However, compared with the azimuth error of the conventional well inclination, the azimuth angle measurement error of the inclinometer under the small angle inclination is larger. In order to improve the azimuth angle measurement accuracy of the inclinometer under the small-angle well inclination, the azimuth angle measurement under the 5°～10° small-angle well inclination was compensated based on the BP neural network algorithm. In this paper, a two-dimensional vector composed of standard well inclination and measured azimuth is used as input, and a one-dimensional vector composed of standard azimuth is used as output, and a network model of double input and single output is established. The learning samples are divided into training set and test set by random selection, so that the network has better generalization ability. The simulation test results show that the BP neural network error correction model runs stably and the compensation accuracy reaches 10 -6, which can improve the measurement accuracy of the azimuth angle under the small-angle well inclination from ±5.3° to within ±1.7°. [ABSTRACT FROM AUTHOR]