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通过优化特征选择,对母胎超声波平面进行新型神经网络分类

S Rathika¹, K Mahendran², H Sudarsan³

¹Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India.

BMC medical imaging

|December 19, 2024

概括

这项研究引入了一种使用混合优化的自动深度学习 (DL) 方法,用于分类母胎超声波 (US) 平面. 这种新的方法提高了产前护理的检测效率和诊断准确性.

科学领域:

医疗成像医学成像
人工智能的人工智能
产科产科产科产科产科

背景情况:

超声波 (美国) 影像对产前护理至关重要,但获得标准的胎儿美国飞机是具有挑战性和耗时的.
需要自动化方法来提高胎儿美国飞机分类的效率和准确性.
人工智能 (AI) 和深度学习 (DL) 为医疗成像挑战提供了有希望的解决方案.

研究的目的:

开发和评估一种基于DL的自动分类方法,用于母胎美国飞机.
通过改进美国飞机分类来提高产前诊断的效率和准确性.
引入用于特征选择的混合优化技术和用于分类的新型辐射基函数神经网络 (RBFNN).

主要方法:

收集和分类了大量的美国母胎图像数据集.
使用灰级共发生矩阵 (GLCM) 进行特征提取.
混合优化 (PSO,GWO,PSOGWO) 用于特征选择,输入常规和拟议的DL模型,包括RBFNN.

主要成果:

建议的混合优化和RBFNN方法在与传统DL模型相比显示出更高的性能.
该方法在与先前公布的模型进行评估时,实现了更高的分类准确性.
实验结果证实了自动分类系统的有效性.

结论:

开发的方法为自动化对美国胎儿飞机的分类提供了坚实的基础.
利用优化和DL技术显著提高产前诊断和护理.
这种自动化系统有望提高临床实践中的检测效率和诊断准确性.

关键词:

准确度准确度准确度准确度准确度数据平衡数据平衡快速辐射基函数神经网络神经网络功能功能功能功能胎儿的器官胎儿的器官优化优化优化优化超声波图像超声波图像超声波图像

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