Skin cancer is one of the top three tumours caused by DNA damage and has a dire prognosis. As a result of this injured DNA, cells start to grow out of control and are currently doubling in size. This study recommends using a hyper-parameter optimised convolution neural net to identify the kind of skin cancer. In this method, the improved elephant herding optimisation (IEHO) algorithm was used to optimise CNN's hyper-parameters. A nonlinear denoising method known as a bilateral filter can lessen noise while maintaining edges. In this study, we frequently use the bilateral filter to modify the texture and noise in skin cancer photo images. An improved elephant herding optimisation has been proposed (IEHO). When the separation operator was changed to the sine–cosine, opposition-based learning was incorporated. The proposed model, which is around 4 and 1% higher than other based models, may produce testing accuracy up to 99.33%, according to simulation results. The experimental findings unequivocally demonstrate that the recommended model outperforms other published models.