A disorder in genetic chromosome 21 is popularly known as the Down syndrome (trisomy 21). It results in development and intellectual delays, but if we are able to give the exact care early on, it can make a good difference. Studies show that we can detect the Down syndrome in the early stages of pregnancy by identifying the absence of the fetal nasal bone. The common method followed in this issue is the visual identification of the ‘absence’ using ultra sonogram image of the nasal bone region. However, the visual identification technique is inefficient and difficult to follow. Thus, image processing based visual extraction technique can play a role in this case. In this paper, we have the raw data, which is employed to train the Back Propagation Neural Network (BPNN). The ultrasonogram images can be analyzed using this feed forward trained neural network and the detection can be made with appreciably low error rates. MATLAB is the platform used in this work for training the Artificial Neural Network (ANN).

Down syndrome, Back Propagation Neural Network, Feature extraction, chromosomal abnormalities

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