Document Type

Article

Publication Date

12-2016

Keywords

pre-trained CNN, transfer learning, deep features, computed tomography, symmetric uncertainty, lung cancer, adenocarcinoma, deep neural network

Digital Object Identifier (DOI)

http://dx.doi.org/10.18383/j.tom.2016.00211

Abstract

Lung cancer is the most common cause of cancer-related deaths in the USA. It can be detected and diagnosed using computed tomography images. For an automated classifier, identifying predictive features from medical images is a key concern. Deep feature extraction using pretrained convolutional neural networks (CNNs) has recently been successfully applied in some image domains. Here, we applied a pretrained CNN to extract deep features from 40 computed tomography images, with contrast, of non-small cell adenocarcinoma lung cancer, and combined deep features with traditional image features and trained classifiers to predict short- and long-term survivors. We experimented with several pretrained CNNs and several feature selection strategies. The best previously reported accuracy when using traditional quantitative features was 77.5% (area under the curve [AUC], 0.712), which was achieved by a decision tree classifier. The best reported accuracy from transfer learning and deep features was 77.5% (AUC, 0.713) using a decision tree classifier. When extracted deep neural network features were combined with traditional quantitative features, we obtained an accuracy of 90% (AUC, 0.935) with the 5 best post-rectified linear unit features extracted from a vgg-f pretrained CNN and the 5 best traditional features. The best results were achieved with the symmetric uncertainty feature ranking algorithm followed by a random forests classifier.

Rights Information

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

TOMOGRAPHY, v. 2, no. 4, p. 388-395

1.1 supplemental appendix.pdf (270 kB)
Supplemental appendix

Additional Files

1.1 supplemental appendix.pdf (270 kB)
Supplemental appendix

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