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2019 | OriginalPaper | Chapter

A Pulmonary Nodule Detection Method Based on Residual Learning and Dense Connection

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Abstract

Pulmonary nodule detection using chest CT scan is an essential but challenging step towards the early diagnosis of lung cancer. Although a number of deep learning-based methods have been published in the literature, these methods still suffer from less accuracy. In this paper, we propose a novel pulmonary module detection method, which uses a 3D residual U-Net (3D RU-Net) for nodule candidate detection and a 3D densely connected CNN (3D DC-Net) for false positive reduction. 3D RU-Net contains residual blocks in both contracting and expansive paths, and 3D DC-Net leverages three dense blocks to facilitate gradients flow. We evaluated our method on the benchmark LUng Nodule Analysis 2016 (LUNA16) dataset and achieved a CPM score of 0.941, which is higher than those achieved by five competing methods. Our results suggest that the proposed method can effectively detect pulmonary nodules on chest CT.
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Metadata
Title
A Pulmonary Nodule Detection Method Based on Residual Learning and Dense Connection
Authors
Feng Zhang
Yutong Xie
Yong Xia
Yanning Zhang
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-33391-1_9

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