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

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, offers an enormous advantage over ML models in accuracy. However, the existing models have focused on down syndrome as a nasal bone (NB) length or nuchal translucency (NT). In this paper, a novel automatic dense convolution neural network (DConN) is proposed to isolate and measure the down syndrome marker particularly NB length and NT. 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 to remove the noise. The pre-processed foetal images are subjected to region of interest extraction to isolate the Down syndrome makers from the ultrasound image. The proposed DConN is used to classify the normal and abnormal foetal based on NT and NB length of the foetal in US images. The performance metrics namely sensitivity, accuracy, specificity, F1 score, and precision are considered for validating the effectiveness of the proposed model. From the experimental the proposed DconN achieves the overall accuracy of 99.01%. 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.