Ultrasound Imaging Department, The Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, China.
J Healthc Eng. 2022 Feb 16;2022:9937051. doi: 10.1155/2022/9937051. eCollection 2022.
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.
分析了宫内妊娠组织残留的腹部 B 超图像，以探讨其诊断价值。随着计算机技术和医学成像技术的飞速发展，医生也面临着越来越多的医学图像诊断任务，计算机辅助诊断系统尤为重要，以减轻医生的工作压力。近年来，深度学习取得了迅猛的发展，并在各个领域取得了重大突破。在医学辅助诊断系统中，深度学习极大地提高了诊断效率，但对于宫内妊娠组织残留的腹部 B 超图像识别尚未有成熟的研究成果。因此，基于深度学习的肝脏超声图像分类研究具有重要的实际应用价值。本文提出了一种基于网格搜索的 CNN 模型优化方法。与传统的 CNN 模型设计相比，该方法通过消除基于经验手动调整参数的需要，节省了时间和精力，对腹部 B 超图像中宫内妊娠组织残留的分类准确率超过 92%。腹部 B 超对宫内妊娠组织残留的诊断可以有效提高诊断率，为患者接受治疗提供重要参考，具有较高的诊断价值。
