Improvement of sensitivity in melanoma detection using artificial bee colony and particle swarm optimization algorithm

The purpose of this research is to enhance the sensitivity of Melanoma diagnosis using an Artificial Bee colony algorithm (ABC). The Artificial Bee Colony Algorithm (ABC) and particle swarm optimization (PSO) are compared in order to determine the sensitivity of Melanoma detection. Materials and methods: Using an online sample size calculator, the study's sample size is computed. The sample size for each group is fifty. The G power utilised in this investigation is 80%, and the alpha value is 0.05. Results: Artificial Bee Colony algorithm based new classification approach gives 75 percent sensitivity when compared to PSO (66 percent ). The statistical significance between two groups, as determined by a two-tailed t-test, is 0.05 (p=0.05).Conclusion: Compared to the PSO method, the Artificial Bee Colony algorithms have shown better outcomes in circumstances of high sensitivity.