Automated Knowledge Extraction of Liver Cysts From CT Images Using Modified Whale Optimization and Fuzzy C Means Clustering Algorithm

Ramanjot Kaur, Baljit Singh Khehra

Source Title: International Journal of Information System Modeling and Design (IJISMD)13(10)

ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781668472262|DOI: 10.4018/IJISMD.306644

MLA

Kaur, Ramanjot, and Baljit Singh Khehra. "Automated Knowledge Extraction of Liver Cysts From CT Images Using Modified Whale Optimization and Fuzzy C Means Clustering Algorithm." IJISMD vol.13, no.10 2022: pp.1-32. http://doi.org/10.4018/IJISMD.306644

APA

Kaur, R. & Khehra, B. S. (2022). Automated Knowledge Extraction of Liver Cysts From CT Images Using Modified Whale Optimization and Fuzzy C Means Clustering Algorithm. International Journal of Information System Modeling and Design (IJISMD), 13(10), 1-32. http://doi.org/10.4018/IJISMD.306644

Chicago

Kaur, Ramanjot, and Baljit Singh Khehra. "Automated Knowledge Extraction of Liver Cysts From CT Images Using Modified Whale Optimization and Fuzzy C Means Clustering Algorithm," International Journal of Information System Modeling and Design (IJISMD) 13, no.10: 1-32. http://doi.org/10.4018/IJISMD.306644

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Abstract

In this study, the integrated modified whale optimization and modified fuzzy c-means clustering algorithm using morphological operations are developed and implemented for appropriate knowledge extraction of a cyst from computer tomography (CT) images of the liver to facilitate modern intelligent healthcare systems. The proposed approach plays an efficient role in diagnosing the liver cyst. To evaluate the efficiency, the outcomes of the proposed approach have been compared with the minimum cross entropy based modified whale optimization algorithm (MCE and MWOA), teaching-learning optimization algorithm based upon minimum cross entropy (MCE and TLBO), particle swarm intelligence algorithm (PSO), genetic algorithm (GA), differential evolution (DE) algorithm, and k-means clustering algorithm. For this, various parameters such as uniformity (U), mean structured similarity index (MSSIM), structured similarity index (SSIM), random index (RI), and peak signal-to-noise ratio (PSNR) have been considered. The experimental results show that the proposed approach is more efficient and accurate than others.