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Title |
A Computer-Aided System for Classifying Computed Tomographic (CT) Lung Images Using Artificial Neural Network and Data Fusion |
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Author |
Hanan M. Amer, Fatma E.Z. Abou-Chadi, Marwa I. Obayya |
| Citation |
Vol. 11 No. 10 pp. 70-75 |
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Abstract |
In this paper, computed tomographic (CT) chest images were investigated to develop a computer-aided system to discriminate different lung abnormalities. These were done by analyzing Data recorded for health issues, and also patients with lung asthma and emphysema diseases were taken in account. The techniques for utilized feature extraction included statistical, morphological features, as well as features derived from texture analysis, Fourier-based features and wavelet-based features. An artificial neural network (ANN) classifier was utilized and the results have shown that using wavelet domain features gives the highest rates to recognize lung abnormalities. Classification correct rate is up to 98.67%. Finally, the classification correct rate can be optimizing using two schemes of data fusion at the decision level, where it was found to reach the classification rate of 99.947%. |
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Keywords |
Computed tomographic, Computer-Aided Diagnosis, Fast Fourier Transform, and Discrete Wavelet Transform, Principal Component Analysis, Artificial Neural Network and Data fusion |
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