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An Investigation of Update Information Equations by using the Artificial Bee Colony Method for Skin Cancer Detection.


Mohanad Aljanabi, Javad Rahebi, Yasa Eksioglu Ozok


Vol. 18  No. 1  pp. 71-78


In Artificial Bee Colony, the one design coefficient of the improvement problem is updated by artificial bees at the Artificial Bee Colony phases by making use of mutual influence of the bees. This updating increased the slow convergence and thus helped to find the best solution for the algorithm. The convergence was set apart by means of a direct and indirect method, and the Artificial Bee Colony was proposed to be used in the Artificial Bee Colony equations. However, more than one design parameters were taken into consideration in this approach to updating. The updating depended on the orders the scout bees were given to find a better solution position after searching more in this area. By varying the new updated information (numbers of iterations) and training the algorithms of random and direct Artificial Bee Colony, the accuracy of this system was enhanced and the parameters were compared. In this study, we used the formal equations to find new positions, new update information, and an optimal solution for good food and behavior of the bees by using both the direct and random Artificial Bee Colony method used in detecting the diseases in the medical system. The proposed method provides the highest accuracy and specificity in the detection of melanoma of all other art methods. The comparison showed that the direct method was very efficient in solving skin cancer detection and successful in terms of the best solution quality and durability. The Artificial Bee Colony algorithm gives the best results in segmenting (melanoma and benign) skin cancer images.


Swarm intelligence, artificial bee colony algorithm, Direct Artificial Bee Colony, Skin cancer detection.