Health Evaluation and Fault Diagnosis of Medical Imaging Equipment Based on Neural Network Algorithm
| Autor: | Weining Jiang, Weidong Gao, Zhenwei Zhao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Diagnostic Imaging
Article Subject General Computer Science Computer science General Mathematics education Computer applications to medicine. Medical informatics R858-859.7 Medical equipment Neurosciences. Biological psychiatry. Neuropsychiatry Fault (power engineering) computer.software_genre Medical imaging Least-Squares Analysis Network model Artificial neural network General Neuroscience General Medicine Identification (information) Test set Data mining Rough set Neural Networks Computer computer human activities Algorithms RC321-571 |
| Zdroj: | Computational Intelligence and Neuroscience, Vol 2021 (2021) |
| ISSN: | 1687-5273 1687-5265 |
| Popis: | In recent years, high-precision medical equipment, especially large-scale medical imaging equipment, is usually composed of circuit, water, light, and other structures. Its structure is cumbersome and complex, so it is difficult to detect and diagnose the health status of medical imaging equipment. Based on the vibration signal of mechanical equipment, a PLSR-DNN hybrid network model for health prediction of medical equipment is proposed by using partial least squares regression (PLSR) algorithm and deep neural networks (DNNs). At the same time, in the diagnosis of medical imaging equipment fault, the paper proposes to use rough set to screen the fault factors and then use BP neural network to classify and identify the fault and analyzes the practical application effect of the two technologies. The results show that the PLSR-DNN hybrid network model for health prediction of medical imaging equipment is basically consistent with the actual health value of medical equipment; medical imaging equipment fault diagnosis technology is based on rough set and BP neural network. In the test set, the sensitivity, specificity, and accuracy of medical imaging equipment fault identification are 75.0%, 83.3%, and 85.0%. The above results show that the proposed health prediction method and fault diagnosis method of medical imaging equipment have good performance in health prediction and fault diagnosis of medical equipment. |
| Databáze: | OpenAIRE |
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