In order to improve the detection and recognition ability of 3D echocardiography, a method of 3D echocardiography detection based on depth learning is proposed. The information conduction model of three-dimensional echocardiography is constructed. The edge pixel feature matching method is used to extract the key information of echocardiography, and the information compensation method is used to repair the missing area of three-dimensional echocardiography information. The feature decomposition and information fusion of 3D ultrasonic imaging are carried out by using five stage wavelet decomposition method, and the feature reconstruction and adaptive template matching of 3D echocardiography are processed by depth learning algorithm, modeling and detecting the rationality of three-dimensional echocardiography. The simulation results show that this method has better detection performance; the accuracy of detection and recognition is high, which is more reasonable in the application of 3D echocardiography repair and detection recognition.

中文翻译：

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基于深度学习的三维超声心动图检测方法

为了提高3D超声心动图的检测和识别能力，提出了一种基于深度学习的3D超声心动图检测方法。建立了三维超声心动图的信息传导模型。边缘像素特征匹配方法用于提取超声心动图的关键信息，信息补偿方法用于修复三维超声心动图信息的缺失区域。利用五阶段小波分解方法进行3D超声成像的特征分解和信息融合，并通过深度学习算法对3D超声心动图进行特征重构和自适应模板匹配，对三维超声心动图进行建模和检测。仿真结果表明，该方法具有较好的检测性能。检测识别的准确性高，在3D超声心动图修复与检测识别的应用中更加合理。