Advancing Deep Learning Techniques in Automated Imaging for Diagnosing Down Syndrome from Ultrasound Foetus Images

Abstract

Various industries have incorporated multiple sectors, like medical with information technology, data analytics-data mining. This paper focused on diagnosing and predicting medical abnormalities based on medical image processing. Ultrasound images are analysed for identifying Down Syndrome (DS), Foetus condition, and other features. Various image segmentation, feature extraction, and classification methods have been in earlier research to diagnose the Foetus in the mother's uterus. Still, the accuracy of the diagnostic accuracy needs to be improved. This paper aimed to conduct an in-depth study and present a literature survey on various aspects. The literature survey can help create a novel methodology for Foetus diagnosis on ultrasound images with increased accuracy. Since the classification accuracy leads to the treatment, improving the severity of the Foetus's abnormality and Down Syndrome is essential.