VGG16-T: A Novel Deep Convolutional Neural Network with Boosting to Identify Pathological Type of Lung Cancer in Early Stage by CT Images

Authors

Shanchen Pang¹, Fan Meng¹, Xun Wang¹, Jianmin Wang^2, , Tao Song^{1, 3, 4, *}, Xingguang Wang^{5, *}, Xiaochun Cheng^{6, *}

¹College of Computer Science and Technology, China University of Petroleum, Qingdao, Shandong, China

²College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China

³School of Electrical Engineering and Automation, Tiangong University, Xiqing, Tianjin, China

⁴Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Campus de Montegancedo Boadilla del Monte, Madrid, Community of Madrid, Spain

⁵Department of Respiratory Medicine, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, China

⁶School of Science and Technology, Middlesex University, The Burroughs, Hendon, London

^*Corresponding authors. Email: t.song@upm.es, xingguang-wang@163.com, x.cheng@mdx.ac.uk

DOI

10.2991/ijcis.d.200608.001How to use a DOI?

Keywords

Pathological type identification; Lung cancer; Data enhancement; Boosting

Abstract

Lung cancer is known as the highest mortality rate cancer, which needs biopsy to determine its subtype for further treatment. Recently, deep learning has provided powerful tools in lung cancer diagnose and therapeutic regimen making. However, it is still a challenge to identify the pathological type of lung cancer in early stage by CT images due to the lack of public training data set and powerful artificial intelligent models. In this work, we firstly build up a data set of CT images from 125 patients of lung cancer in early stage. The data set is enhanced by revolving, shifting and reproducing operations to avoid its inherent imbalance. After that, a deep convolutional neural network namely VGG16-T is proposed and multiple VGG16-T worked as weak classifiers are trained with a boosting strategy. Such method achieves significant performance in identifying pathological type of lung cancer with CT images by joint voting. Experiments conducted on the enhanced data set of CT images show that 3 weak classifiers VGG16-T are sufficient to achieve accuracy 86.58% in identifying pathological type, which performs better than some state-of-the-art deep learning models, including AlexNet, ResNet-34 and DenseNet with or without Softmax weights. As well, VGG16-T is with accuracy 85% by diagnosing 20 randomly selected CT images, while two respiratory doctors from Grade 3A level hospitals obtain accuracy 55% and 65% by handcrafted diagnosing, respectively. To our best acknowledge, this is the first attempt of using deep models and boosting to identify pathological type of lung cancer in early stage from small scale CT images.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

Open Access

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

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TY  - JOUR
AU  - Shanchen Pang
AU  - Fan Meng
AU  - Xun Wang
AU  - Jianmin Wang
AU  - Tao Song
AU  - Xingguang Wang
AU  - Xiaochun Cheng
PY  - 2020
DA  - 2020/06/18
TI  - VGG16-T: A Novel Deep Convolutional Neural Network with Boosting to Identify Pathological Type of Lung Cancer in Early Stage by CT Images
JO  - International Journal of Computational Intelligence Systems
SP  - 771
EP  - 780
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200608.001
DO  - 10.2991/ijcis.d.200608.001
ID  - Pang2020
ER  -