DEEP LEARNING TECHNIQUE BASED FETAL ABNORMALITIES DETECTION USING ULTRASOUND IMAGES

  • Dr. D. Selvathi , Chandralekha R

Abstract

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. The proposed method is realized with Deep Convolutional Neural Network models (DCNN) to find the Region of Interest (ROI) of the fetal biometric and organs region in the Ultrasound image. Based on the ROI, the CNN evaluates 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 CNN. The input sources are trained by CNN and then the process of validation is done by the trained CNN for evaluating the accuracy. The performance of CNN is evaluated with different layers configuration. On the dataset of 200 images used in this classification task, proposed CNN achieves accuracy of 72.67%, 67.50%, 75.00% for 3, 5, 6 hidden layers on 40 validation images with reference to expert’s ground truth results, and on the dataset of 400 images used in this classification task, proposed CNN achieves accuracy of 63.75% 80.01%, 67.25%, for 3, 5, 6 hidden layers on 80 validation images with reference to expert’s ground truth results

Published
2020-04-20
How to Cite
Dr. D. Selvathi , Chandralekha R. (2020). DEEP LEARNING TECHNIQUE BASED FETAL ABNORMALITIES DETECTION USING ULTRASOUND IMAGES. International Journal of Advanced Science and Technology, 29(7s), 1688 - 1697. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/11059