One of the most dangerous cancers in humans is Melanoma. However, early detection of melanoma can help us to cure it completely. This paper presents a new efficient method to detect malignancy in melanoma via images. At first, the extra scales are eliminated by using edge detection and smoothing. Afterwards, the proposed method can be utilized to segment the cancer images. Finally, the extra information is eliminated by morpho- logical operations and used to focus on the area which melanoma boundary potentially exists. To do this, World Cup Optimization algorithm is utilized to optimize an MLP neural Networks (ANN). World Cup Optimization algo- rithm is a new meta-heuristic algorithm which is recently presented and has a good performance in some optimi- zation problems. WCO is a derivative-free, Meta-Heuris- tic algorithm, mimicking the world’s FIFA competitions. World cup Optimization algorithm is a global search algo- rithm while gradient-based back propagation method is local search. In this proposed algorithm, multi-layer per- ceptron network (MLP) employs the problem’s constraints and WCO algorithm attempts to minimize the root mean square error. Experimental results show that the pro- posed method can develop the performance of the stand- ard MLP algorithm significantly.
کلید واژگان :World Cup Optimization Algorithm, Mela- noma, Cancer, Tumors, Artificial Neural Network
ارزش ریالی : 1200000 ریال
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