@article {10.3844/jcssp.2010.634.640,
article_type = {journal},
title = {DWT to Classify Automatically the Placental Tissues Development: Neural Network Approach},
author = {Ayache, Mohammad and Khalil, Mohamad and Tranquart, Francois},
volume = {6},
number = {6},
year = {2010},
month = {Jun},
pages = {634-640},
doi = {10.3844/jcssp.2010.634.640},
url = {https://thescipub.com/abstract/jcssp.2010.634.640},
abstract = {Problem statement: This study proposed an approach for classification of placental tissues development using ultrasound images. Approach: This approach was based to the selection of tissues, feature extraction by discrete wavelet transform and classification by neural network and especially the Multi Layer Perceptron (MLP). Results: The proposed approach was tested for ultrasound placental images; resulting in 95% success rate. Conclusion/Recommendations: The method showed a good recognition for placental tissues and will be useful for detection of the placental anomalies those concerning the premature birth and the intrauterine growth retardation.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}