Adaptive Weighted Vector Means Optimization for Healthy and Malignant Skin Modeling at Microwave Frequencies Using Clinical Data

IF 3 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology Pub Date : 2024-03-25 DOI:10.1109/JERM.2024.3374090
Md. Abdul Awal;Syed Akbar Raza Naqvi;Damien Foong;Amin Abbosh
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Abstract

The dielectric properties of normal and cancerous skin vary with frequency due to changes in water content and tissue composition. Developing a reliable microwave system for skin cancer detection requires accurate characterization of that change in the dielectric properties. A possible choice is the Cole-Cole model, which can accurately fit the measured dielectric data for tissues. However, fitting the non-linear Cole-Cole model parameters with the measured data requires a sophisticated optimization algorithm. This study proposes an adaptive weighted vector means optimization algorithm, which employs adaptive initialization, logarithmic spaces, and enhanced local search mechanism, resulting in improved accuracy with fewer iterations. The algorithm is evaluated using dielectric data from healthy skin, basal cell carcinoma, squamous cell carcinoma, and melanoma and is found to outperform other relevant algorithms. One of the salient features of this study is that a set of clinical melanoma dielectric data is acquired, analyzed, and physically interpreted in terms of relaxation frequency and dispersion across 0.3 GHz to 14 GHz. It is found that melanoma closely follows the second-order Debye model, which is a special case for the second-order Cole-Cole model with a zero-valued dispersion broadening parameter. Although melanoma data is obtained from one lesion because of the low incidence rate, the research findings will contribute to a better understanding skin cancer at microwave frequencies. A triangular plot, which shows model fitness accuracy and the number of iterations, is presented to summarize the advantages of the algorithm.
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利用临床数据对微波频率下健康和恶性皮肤模型进行自适应加权矢量均值优化
由于含水量和组织成分的变化,正常皮肤和癌症皮肤的介电特性会随频率变化。要开发可靠的皮肤癌检测微波系统,就必须准确描述介电特性的这种变化。一种可能的选择是科尔-科尔模型,它能准确拟合组织的测量介电数据。然而,将非线性 Cole-Cole 模型参数与测量数据拟合需要复杂的优化算法。本研究提出了一种自适应加权向量手段优化算法,该算法采用了自适应初始化、对数空间和增强的局部搜索机制,从而以较少的迭代次数提高了精度。利用健康皮肤、基底细胞癌、鳞状细胞癌和黑色素瘤的介电数据对该算法进行了评估,发现其性能优于其他相关算法。本研究的一个显著特点是获取、分析了一组临床黑色素瘤介电数据,并根据 0.3 GHz 至 14 GHz 的弛豫频率和色散进行了物理解释。研究发现,黑色素瘤密切遵循二阶 Debye 模型,该模型是二阶 Cole-Cole 模型的特例,其色散展宽参数为零值。虽然由于发病率低,黑色素瘤的数据是从一个病变中获得的,但研究结果将有助于更好地理解微波频率下的皮肤癌。三角形图显示了模型拟合精度和迭代次数,总结了该算法的优势。
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CiteScore
5.80
自引率
9.40%
发文量
58
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Front Cover Table of Contents IEEE Journal of Electromagnetics, RF, and Microwaves in Medicine and Biology About this Journal IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology Publication Information Models of Melanoma Growth for Assessment of Microwave-Based Diagnostic Tools
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