Liaqat Ali and Khelil Khaled (2017) Machine learning based computer-aided diagnosis of liver tumors. 16th IEEE International Conference on COGNITIVE INFORMATICS & COGNITIVE COMPUTING, , Oxford UK
Scientific Publications
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Abstract
Image processing plays a vital role in the early detection and diagnosis of Hepatocellular Carcinoma (HCC). In this paper, we present a computational intelligence based Computer-Aided Diagnosis (CAD) system that helps medical specialists detect and diagnose HCC in its initial stages. The proposed CAD comprises the following stages: image enhancement, liver segmentation, feature extraction and characterization of HCC by means of classifiers. In the proposed CAD framework, a Discrete Wavelet Transform (DWT) based feature extraction and Support Vector Machine (SVM) based classification methods are introduced for HCC diagnosis. For training and testing, the recorded biomarkers and the associated imaging data are fused. The classification accuracy of the proposed system is critically analyzed and compared with state-of-the-art machine learning algorithms. In addition, laboratory biomarkers are also used to cross-validate the diagnosis.
Information
| Item Type | Conference |
|---|---|
| Divisions |
» Laboratory of Electrical Engineering,Electronic and Renewable Energy |
| ePrint ID | 1373 |
| Date Deposited | 2018-06-29 |
| Further Information | Google Scholar |
| URI | https://univ-soukahras.dz/en/publication/article/1373 |
BibTex
@inproceedings{uniusa1373,
title={Machine learning based computer-aided diagnosis of liver tumors},
author={Liaqat Ali and Khelil Khaled},
year={2017},
booktitle={16th IEEE International Conference on COGNITIVE INFORMATICS & COGNITIVE COMPUTING,}
}
title={Machine learning based computer-aided diagnosis of liver tumors},
author={Liaqat Ali and Khelil Khaled},
year={2017},
booktitle={16th IEEE International Conference on COGNITIVE INFORMATICS & COGNITIVE COMPUTING,}
}