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3 March 2017 Automatic detection of lung nodules: false positive reduction using convolution neural networks and handcrafted features
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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.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ling Fu, Jingchen Ma, Yacheng Ren, Youn Seon Han, and Jun Zhao "Automatic detection of lung nodules: false positive reduction using convolution neural networks and handcrafted features", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340A (3 March 2017);

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