Chronological Dingo Optimizer-based Deep Maxout Network for skin cancer detection and skin lesion segmentation using Double U-Net

Skin cancer is a dreadful disease, which is mainly caused due to the heavy exposure of the human body to the ultraviolet rays emitted from the sun. Although the mortality rate is very high, the survival rate is found to be superior when it is detected at its early stage. In this research, Chronological Dingo Optimizer (CDO)-based Deep Maxout Network (DMN) is developed for the skin cancer detection. Here, initially the images are pre-processed and then, the skin lesion segmentation is accomplished effectively by employing Double U-Net. Then, data augmentation is performed and the detection process is carried out by employing DMN, where the network is optimally fine-tuned utilizing designed CDO. The CDO is the integration of chronological concept with Dingo Optimizer (DOX). The proposed scheme shows better outcomes with superior sensitivity of 0.959, F-measure of 0.908, accuracy of 0.923 and specificity of 0.837.