Brain tumor characterization using the soft computing technique of fuzzy cognitive maps
Brain tumor characterization using the soft computing technique of fuzzy cognitive maps
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DOI：
10.1016/j.asoc.2007.06.006
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发表时间：
2008-01-01
影响因子：
8.7
通讯作者：
Groumpos, P. P.
中科院分区：
文献类型：
--
作者：
Papageorgiou, E. I.;Spyridonos, P. P.;Groumpos, P. P.
脑肿瘤分级的定性和准确确定是非常重要的，因为它影响和规定患者的治疗计划，并最终他的生活。提出了一种新的脑肿瘤特征化方法，该方法模拟了人类的思维方式，并将分类结果与其他计算智能技术进行了比较，证明了该方法的有效性。该方法的新奇是基于使用模糊认知图（FCM）的软计算方法来表示和建模专家的知识（经验，专业知识，启发式）。通过引入计算智能训练技术--激活赫布算法，提高了FCM分级模型的分类能力。所提出的方法进行了验证的临床材料，包括100例。FCM分级模型对低级别和高级别脑肿瘤的诊断准确率分别为90.26%（37/41）和93.22%（55/59）。所提出的分级模型的结果提出了合理的高精度，并与现有的算法，如决策树和模糊决策树，在相同类型的初始数据进行了测试。所提出的FCM分级模型的主要优点是决策过程中的充分可解释性和透明度，这使其成为日常临床实践中表征肿瘤侵袭性的方便咨询工具。(C)2007 Elsevier B. V.保留所有权利。
The characterization and accurate determination of brain tumor grade is very important because it influences and specifies patient's treatment planning and eventually his life. A new method for characterizing brain tumors is presented in this research work, which models the human thinking approach and the classification results are compared with other computational intelligent techniques proving the efficiency of the proposed methodology. The novelty of the method is based on the use of the soft computing method of fuzzy cognitive maps (FCMs) to represent and model experts' knowledge ( experience, expertise, heuristic). The FCM grading model classification ability was enhanced introducing a computational intelligent training technique, the Activation Hebbian Algorithm. The proposed method was validated for clinical material, comprising of 100 cases. FCM grading model achieved a diagnostic output of accuracy of 90.26% ( 37/41) and 93.22% (55/59) for brain tumors of low-grade and high-grade, respectively. The results of the proposed grading model present reasonably high accuracy, and are comparable with existing algorithms, such as decision trees and fuzzy decision trees which were tested at the same type of initial data. The main advantage of the proposed FCM grading model is the sufficient interpretability and transparency in decision process, which make it a convenient consulting tool in characterizing tumor aggressiveness for every day clinical practice. (C) 2007 Elsevier B.V. All rights reserved.