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Lung Nodule Detection in CT Images Using a Raw Patch-Based Convolutional Neural Network

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Abstract

Remarkable progress has been made in image classification and segmentation, due to the recent study of deep convolutional neural networks (CNNs). To solve the similar problem of diagnostic lung nodule detection in low-dose computed tomography (CT) scans, we propose a new Computer-Aided Detection (CAD) system using CNNs and CT image segmentation techniques. Unlike former studies focusing on the classification of malignant nodule types or relying on prior image processing, in this work, we put raw CT image patches directly in CNNs to reduce the complexity of the system. Specifically, we split each CT image into several patches, which are divided into 6 types consisting of 3 nodule types and 3 non-nodule types. We compare the performance of ResNet with different CNNs architectures on CT images from a publicly available dataset named the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). Results show that our best model reaches a high detection sensitivity of 92.8% with 8 false positives per scan (FPs/scan). Compared with related work, our work obtains a state-of-the-art effect.

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Acknowledgments

This work is supported in part by National Natural Science Foundation of China (61772331). We would also like to thank the Shanghai Chest Hospital and department of micro/nanoelectronics at Shanghai Jiao Tong University.

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Authors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, 201100, China

    Qin Wang, Fengyi Shen, Linyao Shen & Weiguang Sheng

  2. Shanghai Chest Hospital, Shanghai, 200030, China

    Jia Huang

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  1. Qin Wang
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  2. Fengyi Shen
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  3. Linyao Shen
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  4. Jia Huang
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  5. Weiguang Sheng
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Correspondence to Weiguang Sheng.

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Wang, Q., Shen, F., Shen, L. et al. Lung Nodule Detection in CT Images Using a Raw Patch-Based Convolutional Neural Network. J Digit Imaging 32, 971–979 (2019). https://doi.org/10.1007/s10278-019-00221-3

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  • DOI: https://doi.org/10.1007/s10278-019-00221-3

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