An efficient approach for detecting downsyndrome fetus images using deep learning method
DOI:
https://doi.org/10.22399/ijcesen.705Keywords:
Fetus, Down syndrome, dataset, deep learning, Nasal boneAbstract
Down Syndrome (DS) is the genetical disorder which can be screened by the ultrasound fetus images. This research work proposes an automated ultrasound fetus image classification system using DL algorithm. This proposed system receives the source ultrasound fetus image and the spatial domain pixels in this image have been converted into multi modal domain pixels using Gabor wavelet. Further, Local Binary Pattern (LBP) has been computed from the multi domain image and these values have been fed into the proposed DL architecture to classify the source fetus image into either normal fetus or abnormal fetus. The morphological algorithm have been used on the abnormal fetus images in order to locate the Nasal Bone (NB) and this proposed DS detection method has been tested on two independent fetus ultrasound datasets Mendeley and Fetal Medicine Foundation (FMF). The proposed NT region segmentation system attains 98.44% NBSe, 98.63% NBSp and 98.62% NBAcc, for the set of 10 fetus images from the Mendeley dataset in this work. The proposed NB region segmentation system attains 98.67% NBSe, 98.66% NBSp and 98.66% NBAcc, for the set of 10 fetus images from the FMF dataset in this work.
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