Enhanced Cuckoo Search Optimization Technique for Skin Cancer Diagnosis Application

Abstract

Skin cancer segmentation is a critical task in a clinical decision support system for skin cancer detection. The suggested enhanced cuckoo search based optimization model will be used to evaluate several metrics in the skin cancer picture segmentation process. Because time and resources are always limited, the proposed enhanced cuckoo search optimization algorithm is one of the most effective strategies for dealing with global optimization difficulties. One of the most significant requirements is to design optimal solutions to optimize their use. There is no particular technique that can answer all optimization issues. The proposed enhanced cuckoo search optimization method indicates a constructive precision for skin cancer over with all image segmentation in computerized diagnosis. The accuracy of the proposed enhanced cuckoo search based optimization for melanoma has increased with a 23% to 29% improvement than other optimization algorithm. The total sensitivity and specificity attained in the proposed system are 99.56% and 99.73% respectively. The proposed method outperforms by offering accuracy of 99.26% in comparisons to other conventional methods. The proposed enhanced optimization technique achieved 98.75%, 98.96% for Dice and Jaccard coefficient. The model trained using the suggested measure outperforms those trained using the conventional method in the segmentation of skin cancer picture data.