Enhanced Cuckoo Search Optimization Technique for Skin Cancer Diagnosis Application

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 pic-ture segmentation process.Because time and resources are always limited,the proposed enhanced cuckoo search optimization algorithm is one of the most effec-tive 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.

基于布谷鸟优化算法的全基因组关联分析

利用贝叶斯网络描述单核苷酸多态性(SNP)与疾病之间的关系,以SNP与疾病之间的贝叶斯评分作为评价SNP与疾病关联度的目标函数,在全基因数据中通过布谷鸟优化算法对SNP与疾病之间的关联进行启发式搜索来寻找致病SNP;通过布谷鸟算法寻找致病SNP可以在保留SNP与疾病相关信息的同时,又能在全基因组数据中高效准确地找出致病SNP。实验结果表明:与已有方法相比,本文基于布谷鸟优化算法的全基因组关联分析方法具有更好的检测SNP与疾病之间关联的能力。