Down syndrome markers classification via dense neural network in ultrasound foetal image

Abstract

Abstract Down syndrome is a genetically born disorder among infants that occurs during the development of the foetus. Trisomy 21, a chromosome imbalance disorder is a leading cause of the Down syndrome. Numerous Machine Learning (ML) models have been used to identify Down syndrome in ultrasound images of foetuses, but the development of Deep Learning (DL), offers an enormous advantage over ML models in accuracy. However, the existing models have focused on Down syndrome as a Nasal bone length or Nuchal translucency. In this paper, an Automatic dense convolution neural network (DConN) is proposed to isolate and measure the Down syndrome marker particularly Nasal bone length and Nuchal translucency. It is necessary to extract texture features precisely from ultrasound images to classify them accurately. Initially, the test image is processed using an Anisotropic Diffusion Filter (ADF) to remove the noise. Then the ROI region is segmented and classified using a dense convolution neural network. The parameters namely sensitivity, accuracy, specificity, F1 score, and precision are considered for validating the effectiveness of the proposed model. The proposed method improves the overall accuracy of 3.9%, 1.6% and 0.41% better than cascaded ML, SIFT+GRNN and Modified AdaBoost respectively.