NDLI: Application of artificial neural networks in the diagnosis of lung cancer by computed tomography

Content Provider	IEEE Xplore Digital Library
Author	Yongjun Wu Na Wang Hongsheng Zhang Lijuan Qin Zhen Yan Yiming Wu
Copyright Year	2010
Description	Author affiliation: College of Public Health, Zhengzhou University, Henan 450001, China (Yongjun Wu; Na Wang; Hongsheng Zhang; Lijuan Qin; Zhen Yan; Yiming Wu)
Abstract	To develop a computer-aided diagnostic scheme of the CT in the diagnosis of lung cancer based on artificial neural networks (ANN) to assist radiologists in distinguishing malignant from benign pulmonary nodules. 117 CT images of pulmonary nodules (58 benign and 59 malignant) were analyzed. 21 CT radiological features of each case were carefully selected and quantified by three experienced radiologists. The 21 features and 5 clinical parameters were used as ANN input data. The result of ANNt was compared with those of logistic regression by ROC curve analysis. The diagnostic accuracy of ANN and logistic regression among all samples of the training group and test group were 96.6% and 84.6%. ANN has the potential to improve the diagnostic accuracy and helpful to radiologists in the distinguishing malignant from benign pulmonary nodules on CT images.
Starting Page	147
Ending Page	153
File Size	353811
Page Count	7
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5583316
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy Computed tomography Lungs Artificial neural networks Cancer Logistics
Content Type	Text
Resource Type	Article

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