首页> 外文期刊>Journal of medical systems >Automatic Detection and Classification of Lung Nodules in CT Image Using Optimized Neuro Fuzzy Classifier with Cuckoo Search Algorithm
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Automatic Detection and Classification of Lung Nodules in CT Image Using Optimized Neuro Fuzzy Classifier with Cuckoo Search Algorithm

机译:用Cuckoo搜索算法使用优化神经模糊分类器的CT图像中肺结节的自动检测和分类

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摘要

The Lung nodules are very important to indicate the lung cancer, and its early detection enables timely treatment and increases the survival rate of patient. Even though lots of works are done in this area, still improvement in accuracy is required for improving the survival rate of the patient. The proposed method can classify the stages of lung cancer in addition to the detection of lung nodules. There are two parts in the proposed method, the first part is used for classifying normal/abnormal and second part is used for classifying stages of lung cancer. Totally 10 features from the lung region segmented image are considered for detection and classification. The first part of the proposed method classifies the input images with the aid of Naive Bayes classifier as normal or abnormal. The second part of the system classifies the four stages of lung cancer using Neuro Fuzzy classifier with Cuckoo Search algorithm. The results of proposed system show that the rate of accuracy of classification is improved and the results are compared with SVM, Neural Network and Neuro Fuzzy Classifiers.
机译:肺结节非常重要,表明肺癌,其早期检测能够及时治疗并增加患者的存活率。即使在该领域完成了许多作品,仍然需要提高患者的存活率所需的准确性。该方法除了检测肺结节之外,还可以对肺癌的阶段进行分类。在该方法中有两部分,第一部分用于分类正常/异常,第二部分用于分类肺癌的阶段。肺区分段图像的完全10个特征被认为是检测和分类。该方法的第一部分借助Naive Bayes分类器作为正常或异常对输入图像进行分类。系统的第二部分使用杜鹃搜索算法使用神经模糊分类器进行肺癌的四个阶段。提出的系统的结果表明,改善了分类的准确性率,结果与SVM,神经网络和神经模糊分类器进行了比较。

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