Lung Cancer Classification Using 3D-CNN with a Scheduled Learning Strategy

DOI

10.2991/icaita-18.2018.41How to use a DOI?

Keywords

3D-CNN; scheduled learning; lung cancer

Abstract

Lung cancer is one of the most common forms of cancer resulting in over a million deaths per year worldwide. In order to classify the lung CT images, this paper presents a classification method using 3D-CNN with a scheduled learning strategy. To get compact and uniform data for training and feature extracting, the input should be unified into 100×100×20 dimension. We construct a 3D-CNN model where a scheduled learning strategy method is proposed in the process of network training. This method is shown to out-perform the state-of-the-art approaches by experiments conducted on the datasets of lung CT scans in Kaggle.

Copyright

© 2018, 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  - Yadi Li
AU  - Yin Tian
AU  - Bao Ge
PY  - 2018/03
DA  - 2018/03
TI  - Lung Cancer Classification Using 3D-CNN with a Scheduled Learning Strategy
BT  - Proceedings of the 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2018)
PB  - Atlantis Press
SP  - 162
EP  - 164
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaita-18.2018.41
DO  - 10.2991/icaita-18.2018.41
ID  - Li2018/03
ER  -