Rathika S, Mahendran K, Sudarsan H, Ananth S Vijay
Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India.
Saveetha Engineering College, Chennai, India.
BMC Med Imaging. 2024 Dec 18;24(1):337. doi: 10.1186/s12880-024-01453-8.
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.
超声（US）成像在产前护理中是一项重要的诊断技术，能够加强对胎儿生长发育的监测。胎儿超声检查标准平面对于评估胎儿发育参数和检测异常情况至关重要。实时成像、低成本、非侵入性和易获得性使得超声成像在临床实践中不可或缺。然而，即使对于经验丰富的超声检查人员来说，获取具有正确胎儿解剖特征的胎儿超声平面也是一项困难且耗时的任务。利用人工智能的医学成像有望应对当前的挑战。针对这一挑战，引入了一种基于深度学习（DL）的母胎超声平面自动分类方法，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，并引入了一种新颖的径向基函数神经网络（RBFNN）用于可靠的母胎超声平面分类。从公开可用的来源收集了一个大型母胎筛查超声图像数据集，并将其分为六组：四个胎儿解剖平面、母亲的宫颈以及一个额外的类别。使用灰度共生矩阵（GLCM）进行特征提取，并利用粒子群优化（PSO）、灰狼优化（GWO）以及粒子群优化与灰狼优化混合（PSOGWO）方法等优化方法来选择最相关的特征。然后将每种算法优化后的特征输入到传统的和提出的深度学习模型中。实验结果表明，所提出的方法在性能上超过了传统的深度学习模型。此外，将所提出的模型与先前发表的模型进行了评估比较，展示了其卓越的分类准确性。总之，我们提出的方法为胎儿超声平面分类自动化提供了坚实的基础，利用优化和深度学习技术来加强产前诊断和护理。
