Accurate Prediction of Fetal Images for measuring growth of Fetus using Genetic Algorithm and Back-Propagation Technique of Neural Network

P. Kaur¹ , G. Singh² , P. Kaur³

1. Department of CET, GNDU, Amritsar, India.
2. Department of CS, GNDU, Amritsar, India.
3. Department of CS, GNDU, Amritsar, India.

Correspondence should be addressed to: prabhpreet.cst@gndu.ac.in.

Section:Research Paper, Product Type: Isroset-Journal
Vol.5 , Issue.3 , pp.30-41, Jun-2017

Online published on Jun 30, 2017

Copyright © P. Kaur, G. Singh, P. Kaur . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Computer Aided Diagnosis (CAD) plays a crucial role in accurately predicting fetal development recently. In this paper, an automatic fetal development measurement as well as classification technique is explained, the goal is to overcome the limitations of accuracy as well as sensitivity in the existing solution of fetal development diagnosis, firstly, the fetal ultrasound image is auto-preprocessed using novel integrated technique, after which texture features like characteristics, Region of Interest (ROI) , as well as background are extracted, and finally, the features are distinguished among abnormal or normal using neuro-fuzzy classifier. Experimental results of proposed technique shows better accuracy rate of classification of 97 % on the benchmark database images with regard to other existing classification methods .The values of sensitivity, specificity, precision rate, recall, F-measure, are much better than those obtained with the other methods. The use of Accuracy (AUC) of Region of Curve (ROC) as assessment indicators is also done to examine the availability of the feature information and the classification accuracy more clearly. These indicators cross-verify the effectiveness of the proposed method.

Key-Words / Index Term :
Ultrasound (US), Artificial Neural Network (ANN), Computer-Aided Diagnostic (CAD), Normal Shrink, Discrete Wavelet Transform (DWT)

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