Novel neural network classification of maternal fetal ultrasound planes through optimized feature selection.

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 2024-12-18 DOI:10.1186/s12880-024-01453-8
S Rathika, K Mahendran, H Sudarsan, S Vijay Ananth
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引用次数: 0

Abstract

Ultrasound (US) imaging is an essential diagnostic technique in prenatal care, enabling enhanced surveillance of fetal growth and development. Fetal ultrasonography standard planes are crucial for evaluating fetal development parameters and detecting abnormalities. Real-time imaging, low cost, non-invasiveness, and accessibility make US imaging indispensable in clinical practice. However, acquiring fetal US planes with correct fetal anatomical features is a difficult and time-consuming task, even for experienced sonographers. Medical imaging using AI shows promise for addressing current challenges. In response to this challenge, a Deep Learning (DL)-based automated categorization method for maternal fetal US planes are introduced to enhance detection efficiency and diagnosis accuracy. This paper presents a hybrid optimization technique for feature selection and introduces a novel Radial Basis Function Neural Network (RBFNN) for reliable maternal fetal US plane classification. A large dataset of maternal-fetal screening US images was collected from publicly available sources and categorized into six groups: the four fetal anatomical planes, the mother's cervix, and an additional category. Feature extraction is performed using Gray-Level Co-occurrence Matrix (GLCM), and optimization methods such as Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and a hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO) approach are utilized to select the most relevant features. The optimized features from each algorithm are then input into both conventional and proposed DL models. Experimental results indicate that the proposed approach surpasses conventional DL models in performance. Furthermore, the proposed model is evaluated against previously published models, showcasing its superior classification accuracy. In conclusion, our proposed approach provides a solid foundation for automating the classification of fetal US planes, leveraging optimization and DL techniques to enhance prenatal diagnosis and care.

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基于优化特征选择的母胎超声平面神经网络分类。
超声(US)成像是产前护理中必不可少的诊断技术,可以增强对胎儿生长发育的监测。胎儿超声标准平面是评估胎儿发育参数和发现异常的关键。实时成像、低成本、非侵入性和可及性使超声成像在临床实践中不可或缺。然而,获取具有正确胎儿解剖特征的胎儿US平面是一项困难且耗时的任务,即使对于经验丰富的超声医师也是如此。使用人工智能进行医学成像有望解决当前的挑战。针对这一挑战,提出了一种基于深度学习(Deep Learning, DL)的母胎US平面自动分类方法,以提高检测效率和诊断准确性。本文提出了一种用于特征选择的混合优化技术,并介绍了一种新的径向基函数神经网络(RBFNN)用于可靠的母胎美平面分类。从公开来源收集了大量母胎筛查美国图像数据集,并将其分为六组:四个胎儿解剖平面,母亲子宫颈和一个额外的类别。利用灰度共生矩阵(GLCM)进行特征提取,并利用粒子群算法(PSO)、灰狼算法(GWO)和粒子群与灰狼混合算法(PSOGWO)等优化方法选择最相关的特征。然后将每个算法的优化特征输入到传统和提议的深度学习模型中。实验结果表明,该方法在性能上优于传统的深度学习模型。此外,根据先前发表的模型对所提出的模型进行了评估,显示了其优越的分类精度。总之,我们提出的方法为胎儿US平面的自动化分类提供了坚实的基础,利用优化和DL技术来增强产前诊断和护理。
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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