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Erschienen in:
2018 | OriginalPaper | Buchkapitel
A Fast Automatic Juxta-pleural Lung Nodule Detection Framework Using Convolutional Neural Networks and Vote Algorithm
verfasst von : Jiaxing Tan, Yumei Huo, Zhengrong Liang, Lihong Li
Erschienen in: Patch-Based Techniques in Medical Imaging
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Abstract
Lung Nodule Detection from CT scans is a crucial task for the early detection of lung cancer with high difficulty performing an automatic detection. In this paper, we propose a fast automatic voting based framework using Convolutional Neural Network to detect juxta-pleural nodules, which are pulmonary (lung) nodules attached to the chest wall and hard to detect even by human experts. The detection result for each region in the CT scan is voted by the detection results of the extracted candidates from the region, which we formulate as a generative model. We perform two sets of experiments: one is to validate our framework, and the other is to compare different convolution neural network settings under our framework. The result shows our framework is competent to detect juxta-pleural lung nodules especially when only a weak classifier trained on noisy data is available. Meanwhile, we overcome the problem of determining the proper input size for nodules with high variance in diameters.