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

• Published in: International journal of computational intelligence systems Vol. 13; no. 1; pp. 771 - 780
• Main Authors: Pang, Shanchen, Meng, Fan, Wang, Xun, Wang, Jianmin, Song, Tao, Wang, Xingguang, Cheng, Xiaochun
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.01.2020; Springer
• Subjects: Boosting; Data enhancement; Lung cancer; Pathological type identification; Research Article; Pathological type identification; Boosting; Data enhancement; Lung cancer
• ISSN: 1875-6891; 1875-6883; 1875-6883
• DOI: 10.2991/ijcis.d.200608.001

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.