Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques
Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques
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DOI：
10.1007/s00521-018-3882-6
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发表时间：
2020-02-01
影响因子：
6
通讯作者：
Burhanuddin, M. A.
中科院分区：
文献类型：
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作者：
Abd Ghani, Mohd Khanapi;Mohammed, Mazin Abed;Burhanuddin, M. A.
鼻咽癌疾病的准确诊断是一项涉及多方面的具有挑战性的任务，如放射科专家往往需要在各种肿瘤内窥镜图像上勾画鼻咽癌的边界。这是一项繁琐和耗时的手术，非常依赖医生和放射科医生的经验。鼻咽癌具有复杂而不规则的结构，即使是内科专家也很难诊断。然而，这些方法的诊断准确率结果仍然不显著，需要改进才能显示出稳健的解。本研究的目的是开发并提出一种新的基于机器学习技术和基于特征决策层融合的鼻咽癌内窥镜图像的自动分类方法。我们已经在决策层实现了三种基于纹理的图像融合方案(局部二值模式、一阶统计直方图特性和灰度直方图)，并使用上一节相同的实验装置对该方案的性能进行了测试，以进行简单的分数级融合，但为了进行比较，我们使用了支持向量机(SVM)、k近邻算法和人工神经网络(ANN)等分类器方法。结果表明，支持向量机分类器的单一最佳性能特征方案明显优于基于决策融合的多数规则，而神经网络和KNN分类器的性能明显优于每个分量特征。分类准确率为94.07%，灵敏度为92.05%，特异度为93.07%。
Making an accurate diagnosis of nasopharyngeal carcinoma (NPC) disease is a challenging task that involves many parties such as radiology specialists often times need to delineate NPC boundaries on various tumor-bearing endoscopic images. It is a tedious and time-consuming operation exceedingly based on doctors and experience of radiologist. NPC has complex and irregular structures which makes it difficult to diagnose even by an expert physician. However, the diagnosis accuracy results of such methods are still insignificant and need improvement in order to manifest robust solution. The study aim is to develop and propose a new automatic classification of NPC tumor using machine learning techniques and feature-based decision-level fusion scheme from endoscopic images. We have implemented the fusion of the three image texture-based schemes (local binary patterns, the first-order statistics histogram properties, and histogram of gray scale) at the decision level and tested the performance of this scheme using the same experimental setup in the previous section for simple score-level fusion, but for comparison, We used the classifiers methods which are support vector machines (SVM), k-nearest neighbors' algorithm, and artificial neural network (ANN). The results demonstrate that the majority rule for decision-based fusion is outperformed considerably by the single best performing feature scheme (FFGF) for the SVM classifier, but for the ANN and KNN classifier it is significantly outperformed by each of the components features. The classifiers approaches were listed a high accuracy of 94.07%, the sensitivity of 92.05%, and specificity of 93.07%.