Abstract
In order to improve the recognition level of abnormal high signal disease, a recognition method of abnormal high signal disease based on machine learning technology is proposed. The abnormal high signal feature extraction method based on machine learning is used to obtain the abnormal high signal. The wavelet threshold method is introduced to remove the noise signal and extract the features. The method based on BP neural network is used to identify disease types. The results show that the identification performance of this method is obviously better than that of similar methods, and the complexity of identification process is only 1.23%. The anti-interference ability of abnormal high signal in the identification of benign and malignant diseases is as high as 0.99, which can effectively eliminate the interference of noise signal, extract the characteristics of abnormal high signal, and complete the disease recognition.
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