Support vector machine combined with magnetic resonance imaging for accurate diagnosis of paediatric pancreatic cancer. Issue 7 (1st April 2020)

• Record Type: Journal Article
• Authors: Zhang, Yuling; Wang, Shuchang; Qu, Shuqiang; Zhang, Hongli
• Is Part Of: IET image processing. Volume 14:Issue 7(2020)
• Page Start: 1233
• Page End: 1239
• Publication Date: 2020-04-01
• Subjects: medical image processing -- learning (artificial intelligence) -- cancer -- patient diagnosis -- paediatrics -- support vector machines -- biomedical MRI; image processing technology -- PC -- computer‐assisted method -- MRI detection model -- algorithm model -- combines image processing -- detection effect -- MRI diagnosis -- support vector machine -- magnetic resonance imaging -- performing paediatric pancreatic cancer diagnosis -- poor diagnosis -- diagnostic effect -- machine learning -- SVM; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1041
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : When performing paediatric pancreatic cancer (PC) diagnosis, magnetic resonance imaging (MRI) will be interfered by many factors, resulting in poor diagnosis. In order to improve the diagnostic effect of MRI on PC, this study is based on machine learning and combines SVM (support vector machine) and MRI detection with the support of image processing technology and improves the diagnostic accuracy of MRI for PC by the computer‐assisted method. At the same time, in the research, this study combines the characteristics of MRI detection to construct an MRI detection model based on SVM. In addition, this study obtains test samples through data collection, analyses the performance of the algorithm model through actual cases, and combines image processing to improve the detection effect. Studies have shown that the algorithm model proposed in this study can effectively improve the accuracy of MRI diagnosis of PC and provide a theoretical reference for subsequent related research.