pre-trained CNN, transfer learning, deep features, computed tomography, symmetric uncertainty, lung cancer, adenocarcinoma, deep neural network
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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.
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Citation / Publisher Attribution
TOMOGRAPHY, v. 2, no. 4, p. 388-395
Scholar Commons Citation
Paul, Rahul; Hawkings, Samuel H.; Schabath, Matthew B.; Gilies, Robert J.; Hall, Lawrence O.; and Goldgof, Dmitry, "Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma" (2016). Computer Science and Engineering Faculty Publications. 106.
Additional Files1.1 supplemental appendix.pdf (270 kB)