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Proceedings Paper

Automatic detection of lung nodules: false positive reduction using convolution neural networks and handcrafted features
Author(s): Ling Fu; Jingchen Ma; Yacheng Ren; Youn Seon Han; Jun Zhao
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Paper Abstract

Lung cancer is the leading cause of cancer deaths worldwide. Early diagnosis is critical in increasing the 5-year survival rate of lung cancer, so the efficient and accurate detection of lung nodules, potential precursors to lung cancer, is evermore important. In this paper, a computer-aided lung nodule detection system using convolution neural networks (CNN) and handcrafted features for false positive reduction is developed. The CNNs were trained with three types of images: lung CT images, their nodule-enhanced images, and their blood vessel-enhanced images. For each nodule candidate, nine 2D patches from differently oriented planes were extracted from each type of images. Patches of the same orientation from the same type of image across different candidates were used to train the CNNs independently, which were used to extract 864 features. 88 handcrafted features including intensity, shape, and texture features were also obtained from the lung CT images. The CNN features and handcrafted features were then combined to train a classifier, and a support vector machine was adopted to achieve the final classification results. The proposed method was evaluated on 1004 CT scans from the LIDC-IDRI database using 10-fold cross-validation. Compared with the traditional CNN method using only lung CT images, the proposed method boosted the sensitivity of nodule detection from 89.0% to 90.9% at 4 FPs/scan and from 71.6% to 78.2% at 1 FP/scan. This indicates that a combination of handcrafted features and CNN features from both lung CT images and enhanced images is a promising method for lung nodule detection.

Paper Details

Date Published: 3 March 2017
PDF: 8 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340A (3 March 2017); doi: 10.1117/12.2253995
Show Author Affiliations
Ling Fu, Shanghai Jiao Tong Univ. (China)
Jingchen Ma, Shanghai Jiao Tong Univ. (China)
Yacheng Ren, Shanghai Jiao Tong Univ. (China)
Youn Seon Han, Johns Hopkins Univ. (United States)
Shanghai Jiao Tong Univ. (China)
Jun Zhao, Shanghai Jiao Tong Univ. (China)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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