Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26708
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Ali, Liaqat
Khelil, Khaled
Wajid, Summrina
Hussain, Zain U
Shah, Moiz Ali
Howard, Adam
Adeel, Ahsan
Shah, Amir A
Sudhakar, Unnam
Howard, Newton
Hussain, Amir
Contact Email: aa55@cs.stir.ac.uk
Title: Machine learning based computer-aided diagnosis of liver tumours
Editor(s): Howard, N
Wang, Y
Hussain, A
Widrow, B
Zadeh, LA
Citation: Ali L, Khelil K, Wajid S, Hussain ZU, Shah MA, Howard A, Adeel A, Shah AA, Sudhakar U, Howard N & Hussain A (2017) Machine learning based computer-aided diagnosis of liver tumours In: Howard N, Wang Y, Hussain A, Widrow B, Zadeh LA (ed.) 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Piscataway, NJ, USA: Institute of Electrical and Electronics Engineers Inc.. 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), 26.7.2017 - 28.7.2017, Oxford, pp. 139-145.
Issue Date: 16-Nov-2017
Conference Name: 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)
Conference Dates: 2017-07-26T00:00:00Z
Conference Location: Oxford
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.
Status: Book Chapter: publisher version
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
URL: http://ieeexplore.ieee.org/abstract/document/8109742/

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