杜鹃搜索和遗传算法在单光子发射计算机断层成像中的应用

2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 1900-01-01 DOI:10.1109/ICCKE.2017.8167898

N. Samadiani, S. Moameri

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引用次数: 3

摘要

冠状动脉疾病(CAD)是一种心血管疾病，心脏病发作是冠心病的第一个征兆。心脏SPECT是诊断该病的有效方法之一。动脉壁上的斑块堆积会导致冠心病，并随着时间的推移使动脉变窄。因此，CAD早期检测的自动化是最重要的问题之一。在文献中，已经提出了各种分类方法。此外，为了降低SPECT图像提取特征的高维数，也发展了许多特征选择技术。本文提出了一种利用SPECT心脏图像进行CAD早期诊断的方法。采用布谷鸟搜索和遗传算法选择最优特征集，将特征向量维数从44个特征降至5个特征。采用Bagging算法对SPECT数据进行分类，检出率达到77.19%。实验结果表明，该方法与现有研究方法相比具有较高的性能。

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Diagnosis of Coronary Artery Disease using Cuckoo Search and genetic algorithm in single photon emision computed tomography images

Coronary Artery Disease (CAD) is a kind of cardiovascular disease and a heart attack is the first sign of CAD. Cardiac SPECT is one of the efficient methods to diagnose the disease. Plaque buildup in the walls of the arteries causes CAD and makes them narrow over time. Therefore, one of the most important issues is automating of CAD early detection. In the literature, various classification methods have been presented. Also, a lot of feature selection techniques have been developed to reduce the high dimension of extracted features of images in SPECT. In this paper, a method has been proposed for early diagnosis of CAD from SPECT heart images. The Cuckoo Search and Genetic algorithm are employed for selecting the optimal set of features which can lessen feature vector dimension from 44 to 5 features. Detection rate of 77.19% is obtained by using Bagging algorithm for classifying SPECT data. Results show the proposed method has high performance comparing with other recently researches.

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2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)

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