The classification of fetus gender on ultrasound images using learning vector quantization (LVQ)

Abstract

One example of the implementations of digital image processing in biomedical field is to identify the gender of the fetus on the ultrasound image. To identify the gender of the fetus, a fetal must attain the age of at least 5 months of pregnancy. Before the process of identification, there are three steps that must be done, i.e. image preprocessing, image segmentation, and feature extraction (shape description). Having obtained the value of the feature extraction stage, the next step is the classification by utilizing one of the artificial neural network (ANN) methods, namely the learning vector quantization (LVQ). Prior to the LVQ process, the training datasets process is conducted beforehand with 3 iterations using the learning rate of 0.05 and the learning rate reduction of 0.02 per iteration. Then the training process is followed by a classification stage. The obtained test results show that the LVQ classification gives poor results. The less optimal results are generated due to the quality of the dataset used. The quality of this dataset is affected by the results of the digitization process, the stage of preprocessing, segmentation, and feature extraction.