Application of Modified Inception-ResNet and CondenseNet in Lung Nodule Classification

DOI

10.2991/iccia-19.2019.28How to use a DOI?

Keywords

Inception-ResNet; CondenseNet; lung nodule classification; SENet; 3D convolution.

Abstract

Nowadays lung cancer is one of the most vital malignancies to human health. Restricted by current medical technology, the best solution to lung cancer is still early diagnosis and targeted treatment. Lung nodule is early clinical sign of lung cancer, and low-dose spiral computed tomography is widely considered to be the most effective approach for lung cancer early screening. Through increasing accuracy and stability of diagnosis, CAD can significantly improve quality and efficiency of medical image analysis, reduce the chance of wrong diagnosis caused by subjective factors and missed diagnosis. With rapid development of CNN in image processing, there emerge many kinds of CNN architectures which achieve outstanding performance in image classification. We select Inception-ResNet and CondenseNet as candidate networks due to their outstanding classification performance in ImageNet. Consider reliance among feature map channels and 3D nature of CT scans, 3D-SE-IRNet and 3D-SE-IRNet were designed to further improve accuracy of networks. Results of our experiment prove good performance of CNNs in lung nodule CT scans classification. What’s more, introducing self-mechanism and 3D convolution can significantly improve network’s accuracy.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jingzhi Fu
PY  - 2019/07
DA  - 2019/07
TI  - Application of Modified Inception-ResNet and CondenseNet in Lung Nodule Classification
BT  - Proceedings of the 3rd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2019)
PB  - Atlantis Press
SP  - 186
EP  - 194
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccia-19.2019.28
DO  - 10.2991/iccia-19.2019.28
ID  - Fu2019/07
ER  -