International Journal of Computational Intelligence Systems

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Volume 13, Issue 1, 2020, Pages 1507 - 1516

Cancer Cell Detection through Histological Nuclei Images Applying the Hybrid Combination of Artificial Bee Colony and Particle Swarm Optimization Algorithms

Authors
Faozia Ali Alsarori1, Hilal Kaya1, Javad Rahebi2, Daniela E. Popescu3, D. Jude Hemanth4, *
1Department of Computer Engineering, Yıldırım Beyazıt University, Ankara, Turkey
2Department of Electrical and Computer Engineering, Altınbaş University, Istanbul, Turkey
3Faculty of Electrical Engineering and Information Technology, University of Oradea, Oradea, Romania
4Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, India
*Corresponding author. Email: judehemanth@karunya.edu
Corresponding Author
D. Jude Hemanth
Received 6 June 2020, Accepted 8 September 2020, Available Online 22 September 2020.
DOI
10.2991/ijcis.d.200915.003How to use a DOI?
Keywords
Artificial bee colony; Image processing; Nuclei segmentation; PSO
Abstract

Cancer is a fatal disease that is continuously growing in the developed countries. It is also considered as a main global human health problem. Based on several studies, which have been conducted so far, we found out that Hybrid Particle Swarm Optimization and Artificial Bee Colony Algorithm has never been used in any relevant study; so, in this study we purposed using this algorithm for detecting the centers of the nuclei with the help of histological images to obtain accurate results. If we compare this algorithm with previously proposed algorithms, this algorithm doesn't require a lot of parameters, and besides, it is faster, simpler, and more flexible. This study has been carried out on histological images obtained from a database containing 810 microscopic slides of stained H&E samples from PSB-2015 crowd-sourced nuclei dataset. During the determination process, the noise on images was first eliminated using morphological techniques, and then, we used Hybrid PSO-ABC algorithm to for segmentation of the nucleic images and compared the results with other optimization algorithms to test its accuracy and efficiency. The average 99.38% accuracy rate was assured for cancer nuclei. To demonstrate the robustness of this experiment, the results were compared with other known cancer nuclei detection procedures, which are already mentioned in the literature. Using the new proposed algorithm showed the highest accuracy when it was compared to rest of the methods. The high value outcome confirms that the suggested method outperformed as compared to other algorithms because it shows a higher distinctive ability.

Copyright
© 2020 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
13 - 1
Pages
1507 - 1516
Publication Date
2020/09/22
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.200915.003How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Faozia Ali Alsarori
AU  - Hilal Kaya
AU  - Javad Rahebi
AU  - Daniela E. Popescu
AU  - D. Jude Hemanth
PY  - 2020
DA  - 2020/09/22
TI  - Cancer Cell Detection through Histological Nuclei Images Applying the Hybrid Combination of Artificial Bee Colony and Particle Swarm Optimization Algorithms
JO  - International Journal of Computational Intelligence Systems
SP  - 1507
EP  - 1516
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200915.003
DO  - 10.2991/ijcis.d.200915.003
ID  - Alsarori2020
ER  -