Down syndrome detection using modified adaboost algorithm

Abstract

In human body genetic codes are stored in the genes. All of our inherited traits are associated with these genes and are grouped as structures generally called chromosomes. In typical cases, each cell consists of 23 pairs of chromosomes, out of which each parent contributes half. But if a person has a partial or full copy of chromosome 21, the situation is called Down syndrome. It results in intellectual disability, reading impairment, developmental delay, and other medical abnormalities. There is no specific treatment for Down syndrome. Thus, early detection and screening of this disability are the best styles for down syndrome prevention. In this work, recognition of Down syndrome utilizes a set of facial expression images. Solid geometric descriptor is employed for extracting the facial features from the image set. An AdaBoost method is practiced to gather the required data sets and for the categorization. The extracted information is then assigned and used to instruct the Neural Network using Backpropagation algorithm. This work recorded that the presented model meets the requirement with 98.67% accuracy.

An Intelligent and Informatics e-Health at-Birth Detection and Monitoring System for Children born with Down Syndrome in Rwanda: Design and Implementation

“…Telemedicine offers a promising solution for patient interaction, enabling healthcare providers to gather clinical signs and symptoms, enabling doctors to collaborate on diagnosis and management plans using synchronous or asynchronous networks [22]. DS detection approach using modified Adaboost algorithm was developed in [23]. The approach for identifying DS employs expressive facial imagery, which is obtained with an accurate geometrical classifier and categorized via the Adaboost technical algorithm.…”