Early in pregnancy, ultrasounds are used to confirm the fetal heartbeat and a uterine pregnancy. Later, ultrasounds screen for fetal growth, placenta location and umbilical cord, as well as the baby's general health and anatomy. Identifying and interpreting fetal standard scan planes during 2-D ultrasound mid-pregnancy examinations are highly complex tasks, which require years of training. Apart from guiding the probe to the correct location, it is equally difficult for a non-expert to identify relevant structures within the image. The procedure requires a sonographer to find the standardized visualization planes with a probe and manually place measurement calipers on the structures of interest. The process is tedious, time consuming, and introduces user variability into the measurements. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. The proposed method is realized with deep convolutional neural network models to find the region of interest (ROI) of the fetal biometric and organs region in the US image. Based on the ROI, AlexNet, GoogleNet and CNN evaluate the image quality by assessing the goodness of depiction for the key structures of fetal biometrics. In this method both normal and abnormal US data are considered. In addition with that the input sources of the neural network are augmented with the local phase features along with the original US data. These augmented input sources helps to improve the performance of the various Neural Networks. The input sources are trained by AlexNet, GoogleNet and CNN. Then the process of validation is done by performance in proposed Networks for evaluating the accuracy. The performance of proposed work is evaluated with different network configuration. On the dataset of 400 images used in this classification task, proposed work of AlexNet, GoogleNet and CNN achieves accuracy of 90.43%, 88.70%, and 81.25% with reference to expert’s ground truth results respectively.

在怀孕初期，超声波用于确认胎儿心跳和子宫妊娠。后来，超声波检查会检查胎儿的成长，胎盘的位置和脐带，以及婴儿的总体健康状况和解剖结构。在2D超声妊娠中期检查期间识别和解释胎儿标准扫描平面是一项非常复杂的任务，需要多年的培训。除了将探针引导到正确的位置之外，非专家同样很难识别图像中的相关结构。该过程需要超声检查人员用探头找到标准化的可视化平面，然后将测量卡尺手动放置在目标结构上。该过程繁琐，耗时，并且将用户的可变性引入到测量中。自动图像处理可以提供工具，以帮助有经验的和无经验的操作员完成这些任务。所提出的方法是通过深度卷积神经网络模型实现的，以在美国图像中找到胎儿生物特征和器官区域的感兴趣区域（ROI）。基于投资回报率（AlexROI），AlexNet，GoogleNet和CNN，通过评估胎儿生物特征识别关键结构的描绘良好性来评估图像质量。在这种方法中，正常和异常的美国数据都被考虑了。此外，神经网络的输入源还增加了局部相位特征以及原始的美国数据。这些增强的输入源有助于改善各种神经网络的性能。输入源由AlexNet，GoogleNet和CNN训练。然后，通过提议的网络中的性能来完成验证过程，以评估准确性。拟议工作的性能是通过不同的网络配置进行评估的。在分类任务中使用的400张图像的数据集上，AlexNet，GoogleNet和CNN的拟议工作分别达到90.43％，88.70％和81.25％的准确度（参考专家的地面真实结果）。