Neural Network Approach to Classify Automatically the Placental Tissues Development: MLP and RBF


This paper proposes an efficient method for the classification of placental development with normal tissues. The proposed method consists of selection of tissues, feature extraction using discrete wavelet transform and classification of the tissue by the multi layer perceptron MLP and radial basis function RBF. The method is tested for placental images acquired by ultrasound techniques; resulting in 95% success rate. The proposed method showed a good classification rate. The method will be useful for detection of the anomalies those concerning premature birth and intra-uterine growth retardation.

  • Abstract
  • I. Introduction
  • II. Problem Statement
  • III. Discrete wavelet transform
  • IV. Artificial neural networks
  • V. Results and discussions
  • VI. Conclusion
  • VII. References

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In