Nuclear magnetic resonance spectrum aided diagnosis based on DNN neural network

Abstract

The nuclear magnetic resonance spectrum contains a variety of effective information, and most of the current clinical medicine uses nuclear magnetic resonance images as a diagnostic basis, but the spectral information is still not effectively explored. In order to improve the diagnostic results of nuclear magnetic resonance spectrum, this study uses DNN neural network as a technical support to extract effective information of nuclear magnetic resonance spectrum. Simultaneously, in order to improve the ability to describe the local features of the image, the traditional Crow algorithm is improved, and a similar target localization algorithm based on F-CroW is proposed. In addition, starting from the animal model of nasopharyngeal carcinoma and the serum and urine samples of patients with clinical nasopharyngeal carcinoma, this study designed a comparative study to study the performance of the proposed algorithm. According to the research and analysis, the DNN neural network proposed in this study has certain effects in the nuclear magnetic resonance spectrum analysis, which can be applied to clinical practice.