B-Ultrasound Image Analysis of Intrauterine Pregnancy Residues after Mid-Term Pregnancy Based on Smart Medical Big Data. (16th February 2022)

• Record Type: Journal Article
• Authors: He, Huiliao; Liu, Ruixing; Zhou, Xiuping; Zhang, Yinhong; Yu, Beibei; Xu, Zhihua; Huang, Hu
• Other Names: Rajakani Kalidoss Academic Editor.
• Is Part Of: Journal of healthcare engineering. Volume 2022(2022)
• Publication Date: 2022-02-16
• Subjects: Hospital buildings -- Environmental engineering -- Periodicals; Medical technology -- Periodicals; Medical informatics -- Periodicals; 610.28
• DOI: 10.1155/2022/9937051
• Languages: English
• ISSNs: 2040-2295

Abstract

Abstract : Abdominal B-ultrasound images of intrauterine pregnancy tissue residues were analyzed to discuss their diagnostic value. With the rapid development of computer technology and medical imaging technology, doctors are also faced with more and more medical image diagnosis tasks, and computer-aided diagnosis systems are especially important in order to reduce the work pressure of doctors. In recent years, deep learning has made rapid development and achieved great breakthroughs in various fields. In medical-aided diagnostic systems, deep learning has greatly improved the diagnostic efficiency, but there are no mature research results for abdominal B-ultrasound image recognition of intrauterine pregnancy tissue residues. Therefore, the study of liver ultrasound image classification based on deep learning has important practical application value. In this paper, we propose to give a CNN model optimization method based on grid search. Compared with the conventional CNN model design, this method saves time and effort by eliminating the need to manually adjust parameters based on experience and has an accuracy of more than 92% in classifying abdominal B-ultrasound images of intrauterine pregnancy tissue residues. The diagnosis of intrauterine pregnancy tissue residues by abdominal B-ultrasound can effectively improve the diagnosis and provide important reference for patients to receive treatment, which has high diagnostic value.