Neural network combining X-ray and ultrasound in breast examination
文献类型：期刊论文
| 作者 | Song, Jiaguang2; Zhang, Yuezhong2; Wang, Shi3; Liu, Zhi4; Sun, Dianmin1,5 |
| 刊名 | NEURAL COMPUTING & APPLICATIONS
 |
| 出版日期 | 2021-03-28 |
| 页码 | 13 |
| ISSN号 | 0941-0643 |
| 关键词 | Neural network X-ray examination Ultrasound examination AdaBoost integrated neural network |
| DOI | 10.1007/s00521-021-05882-2 |
| 英文摘要 | With the acceleration of people's life rhythm, the incidence of breast cancer is gradually increasing. This study mainly explores the application of neural network combined with X-ray and ultrasound in breast examination. The process discussed in this research is mainly based on AdaBoost integrated neural network. According to the basic principles of artificial neural networks, first use training samples to train the neural network, then form an integrated neural network based on the AdaBoost algorithm, and then use the test samples to perform prediction tests on the network, and perform calculations in the integrated neural network to predict the classification the results are compared with the actual results in the test set, and the total number of misclassifications is analyzed to determine whether the prediction effect of the integrated neural network is good or bad. In the process of training the neural network, the analysis of the obtained case data needs to be numerically processed and then normalized to map the data to the [- 1,1] interval. The segmentation goal is to segment each tissue in the breast ultrasound image, which are skin layer, fat layer, gland layer, tumor, muscle layer, and background layer. The mean squared error is used as the network performance evaluation function. The research results are measured by the error rate. The lower the error rate, the better the classification prediction effect and the better the performance of the individual neural network. The diagnostic coincidence rate combined with X-ray and ultrasound was 88.33% (265/300). Compared with pure ultrasound or mammography, the difference was significant (P < 0.05). The recall rate of neural network combined with X-ray and ultrasound reached 91.4%. The results show that the neural network combined with X-ray and ultrasound has extremely high application value in breast examination. |
| 资助项目 | Key Research and Development Program of Shandong Province[2019JZZY011101] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| 出版者 | SPRINGER LONDON LTD |
| WOS记录号 | WOS:000634309600001 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/16770]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Sun, Dianmin |
| 作者单位 | 1.Shandong First Med Univ, Shandong Canc Hosp & Inst, Dept Thorac Surg, Jinan 250117, Shandong, Peoples R China 2.Shandong First Med Univ, Shandong Prov Hosp, Dept Ultrasound, Jinan 250117, Shandong, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 4.Shandong Univ, Sch Informat Sci & Engn, Qingdao 266237, Shandong, Peoples R China 5.Shandong Univ, Shandong Acad Med Sci, Jinan 250117, Shandong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Song, Jiaguang,Zhang, Yuezhong,Wang, Shi,et al. Neural network combining X-ray and ultrasound in breast examination[J]. NEURAL COMPUTING & APPLICATIONS,2021:13. |
| APA | Song, Jiaguang,Zhang, Yuezhong,Wang, Shi,Liu, Zhi,&Sun, Dianmin.(2021).Neural network combining X-ray and ultrasound in breast examination.NEURAL COMPUTING & APPLICATIONS,13. |
| MLA | Song, Jiaguang,et al."Neural network combining X-ray and ultrasound in breast examination".NEURAL COMPUTING & APPLICATIONS (2021):13. |
入库方式： OAI收割
来源：计算技术研究所
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