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Lung nodule detection of CT images based on combining 3D-CNN and squeeze-and-excitation networks

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Abstract

Malignant lung nodules are the worse stage for lung cancer patients. Early detection of lung nodules is essential for early treatment of the patients and significantly improves their survival rate. Recently, several computer-aided diagnosis (CAD) schemes based on deep learning approaches have been suggested to assist in lung nodule diagnosis. This study presents a 3D U-shaped encoding and decoding deep convolutional neural network (CNN) integrated with channel attention mechanisms for lung nodule detection in chest CT images. The U-shaped network applies the encoder to extract nodule representation features and the decoder to indicate the prediction results of the nodule candidates. Hybrid efficient channel attention (ECA) modules are integrated to enhance network representation power by retaining rich contextual nodule information and suppressing useless features. Correspondingly, the 3D regional proposal network (RPN) with three anchor boxes is employed for multilevel nodule candidates detection. The Lung Nodule Analysis 2016 (LUNA16) dataset was used to validate the proposed method. Experimental results show that through 10-fold cross-validation, the proposed algorithm gives the highest detection sensitivity of 98.65% and the competition performance metric (CPM) value of 0.911. Therefore, the results of our design show competitiveness compared with the methods in the prior studies for lung nodule detection in chest CT images.

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Data availability

The research work uses the LUNA16 dataset that is available online through https://luna16.grand-challenge.org/

Code availability

Custom codes.

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Acknowledgments

We would love to thank the Editors and synonyms reviewers for their constructive comments on improving our work.

Funding

This work is supported by The National Natural Science Foundation of China under Grant numbers 61671185 and 62071153.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, 150001, China

    Hassan Mkindu, Longwen Wu & Yaqin Zhao

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  1. Hassan Mkindu
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  2. Longwen Wu
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Correspondence to Longwen Wu.

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Mkindu, H., Wu, L. & Zhao, Y. Lung nodule detection of CT images based on combining 3D-CNN and squeeze-and-excitation networks. Multimed Tools Appl 82, 25747–25760 (2023). https://doi.org/10.1007/s11042-023-14581-0

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