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

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.