Efficient Computer Aided Diagnosis System for Hepatic Tumors Using Computed Tomography Scans

Yasmeen Al-Saeed; Wael Gab-Allah; Hassan Soliman; Maysoon Abulkhair; Wafaa Shalash; Mohammed Elmogy

doi:10.32604/cmc.2022.023638

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ARTICLE

Efficient Computer Aided Diagnosis System for Hepatic Tumors Using Computed Tomography Scans

Yasmeen Al-Saeed^1,2, Wael A. Gab-Allah¹, Hassan Soliman¹, Maysoon F. Abulkhair³, Wafaa M. Shalash⁴, Mohammed Elmogy^1,*
1 Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt
2 Faculty of Computers & Artificial Intelligence, South Valley University, Hurghada, 84511, Egypt
3 Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
4 Faculty of Computers and Artificial Intelligence, Benha University, Banha, 13511, Egypt
* Corresponding Author: Mohammed Elmogy. Email:

Computers, Materials & Continua 2022, 71(3), 4871-4894. https://doi.org/10.32604/cmc.2022.023638

Received 15 September 2021; Accepted 05 November 2021; Issue published 14 January 2022

Abstract

One of the leading causes of mortality worldwide is liver cancer. The earlier the detection of hepatic tumors, the lower the mortality rate. This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors. Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range, intensity values overlap between the liver and neighboring organs, high noise from computed tomography scanner, and large variance in tumors shapes. The proposed method consists of three main stages; liver segmentation using Fast Generalized Fuzzy C-Means, tumor segmentation using dynamic thresholding, and the tumor's classification into malignant/benign using support vector machines classifier. The performance of the proposed system was evaluated using three liver benchmark datasets, which are MICCAI-Sliver07, LiTS17, and 3Dircadb. The proposed computer adided diagnosis system achieved an average accuracy of 96.75%, sensetivity of 96.38%, specificity of 95.20% and Dice similarity coefficient of 95.13%.

Keywords

Liver tumor; hepatic tumors diagnosis; CT scans analysis; liver segmentation; tumor segmentation; features extraction; tumors classification; FGFCM; CAD system

Cite This Article

Y. Al-Saeed, W. A. Gab-Allah, H. Soliman, M. F. Abulkhair, W. M. Shalash et al., "Efficient computer aided diagnosis system for hepatic tumors using computed tomography scans," Computers, Materials & Continua, vol. 71, no.3, pp. 4871–4894, 2022.

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