期刊
TOMOGRAPHY
卷 2, 期 4, 页码 388-395出版社
MDPI
DOI: 10.18383/j.tom.2016.00211
关键词
pre-trained CNN; transfer learning; deep features; computed tomography; symmetric uncertainty; lung cancer; adenocarcinoma; deep neural network
资金
- National Institutes of Health [U01 CA143062, U24 CA180927]
- National Science Foundation [1513126]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1513126] Funding Source: National Science Foundation
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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