Lung Nodule Classification Using Deep Features in CT Images

被引：255

作者：

Kumar, Devinder ^{[1
]}

Wong, Alexander ^{[1
]}

Clausi, David A. ^{[1
]}

机构：

[1] Univ Waterloo, Syst Design Engn, Waterloo, ON, Canada

来源：

2015 12TH CONFERENCE ON COMPUTER AND ROBOT VISION CRV 2015 | 2015年

关键词：

Computer-aided diagnosis (CAD); LIDC; deep features; autoencoder; lung nodule; COMPUTER-AIDED DETECTION; DATABASE CONSORTIUM; PULMONARY NODULES; REPRESENTATION; CANCER; ALGORITHMS; RESOURCE; SCANS;

D O I：

10.1109/CRV.2015.25

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Early detection of lung cancer can help in a sharp decrease in the lung cancer mortality rate, which accounts for more than 17% percent of the total cancer related deaths. A large number of cases are encountered by radiologists on a daily basis for initial diagnosis. Computer-aided diagnosis (CAD) systems can assist radiologists by offering a second opinion and making the whole process faster. We propose a CAD system which uses deep features extracted from an autoencoder to classify lung nodules as either malignant or benign. We use 4303 instances containing 4323 nodules from the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset to obtain an overall accuracy of 75.01% with a sensitivity of 83.35% and false positive of 0.39/patient over a 10 fold cross validation.

引用

页码：133 / 138

页数：6

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