Down syndrome identification and classification using facial features with neural network
1 Department of Computer Science, Bharathiar University, Coimbatore, India.
2 Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India.
Research Article
Global Journal of Engineering and Technology Advances, 2022, 12(01), 001–011.
Article DOI: 10.30574/gjeta.2022.12.1.0090
Publication history:
Received on 15 May 2021; revised on 23 June 2021; accepted on 26 June 2021
Abstract:
The medical image tool named Ultrasound imaging is used for the analysis of fetus behaviour. Due to the presence of noise the quality of these images is low. Proper filtering is required to suppress this noise. Before the fatal development analysis, it is required image processing method. The processed images are then used for the analysis and it helps to take care of the health. Down syndrome is one of the crucial chromosomal disorders. It is due to the presence of a further copy of chromosome 21. The ultrasound imaging helps to point out DS in the earlier stage of pregnancy by handling the image in an efficient manner. In this work, nasal bone identification and texturing methods are used to detect the disease. Here we designed a nasal bone identification module for the image classification as normal or abnormal. Down syndrome detection utilizes a collection of facial expression images. A compact geometric descriptor is employed for extracting the facial features from the image set. There is no specific treatment for Down syndrome. Thus, early detection and screening of this disability are the best styles for Down syndrome prevention.
Keywords:
Ultrasound image; Pre-processing; Down syndrome; Nasal bone identification; Classifications; Artificial neural network
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0