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首页> 外文期刊>Biocybernetics and biomedical engineering >An optimal spectroscopic feature fusion strategy for MR brain tumor classification using Fisher Criteria and Parameter-Free BAT optimization algorithm
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An optimal spectroscopic feature fusion strategy for MR brain tumor classification using Fisher Criteria and Parameter-Free BAT optimization algorithm

机译：利用Fisher标准和无参数蝙蝠优化算法的最佳光谱特征融合策略

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摘要

In the present work, a fused metabolite ratio is proposed that integrates the conventional metabolite ratios in a weighted manner to improve the diagnostic accuracy of glioma brain tumor categorization. Each metabolite ratio is weighted by the value generated by the Fisher and the Parameter-Free BAT (PFree BAT) optimization algorithm. Here, feature fusion is formulated as an optimization problem with PFree BAT optimization as its underlying search strategy and Fisher Criterion serving as a fitness function. Experiments were conducted on the magnetic resonance spectroscopy (MRS) data of 50 subjects out of which 27 showed low-grade glioma and rest presented high-grade. The MRS data was analyzed for the peaks. The conventional metabolite ratios, i.e., Choline/N-acetyl aspartate (Cho/NAA), Cho/Creatine (Cho/Cr), were quantitated using peak integration that exhibited difference among the tumor grades. The difference in the conventional metabolite ratios was enhanced by the proposed fused metabolite ratio that was duly validated by metrics of sensitivity, specificity, and the classification accuracy. Typically, the fused metabolite ratio characterized low-grade and high-grade with a sensitivity of 96%, specificity of 91%, and an accuracy of 93.72% when fed to the K-nearest neighbor classifier following a fivefold cross-validation data partitioning scheme. The results are significantly better than that obtained by the conventional metabolites where an accuracy equal to 80%, 87%, and 89% was attained. Prominently, the results using the fused metabolite ratio show a surge of 4.7% in comparison to Cho/Cr + Cho/NAA + NAA/Cr. Moreover, the obtained results are better than the similar works reported in the literature. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
机译：在本作本作中，提出了一种融合的代谢物比，其以加权方式整合常规的代谢物比以提高胶质瘤脑肿瘤分类的诊断准确性。每种代谢物比率由渔业产生的值和无参数蝙蝠（PREEE BAT）优化算法加权。在这里，特征融合作为其底层搜索策略和作为健身功能的底层搜索策略和Fisher标准，将特征融合作为优化问题。在50个受试者的磁共振光谱（MRS）数据上进行了实验，其中27个胶质瘤显示出低级胶质瘤和休息呈现高等级。为峰值分析了MRS数据。使用在肿瘤等级中表现出差异的峰积分来定量常规代谢物比例，即胆碱/ N-乙酰天使（CHO / NAA），CHO / CREARINE（CHO / CR）。通过敏感性，特异性和分类准确性的度量正式验证的拟议融合代谢物比率，增强了常规代谢菌素比的差异。通常，稠合的代谢物比表征低级和高级，灵敏度为96％，特异性为91％，并且在五倍交叉验证数据分区方案之后向K-最近邻分类器馈送到k最近邻分类时的精度为93.72％ 。结果明显优于通过常规代谢物获得的结果，其中达到等于80％，87％和89％的精度。突出的是，与CHO / Cr + Cho / Naa + Naa / Cr相比，使用稠合代谢物比的结果表明4.7％的浪涌。此外，所获得的结果优于文献中报告的类似作品。 （c）2018年纳雷斯州博士生物庭院研究所和波兰科学院的生物医学工程。 elsevier b.v出版。保留所有权利。

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