A Hybrid Deep Learning Approach for Lung Nodule Classification
DOI:
https://doi.org/10.54097/498fxm65Keywords:
Lung Nodule Benign and Malignant Classification, Deep learning, ResNet, Convolutional Neural Network, VGGNetAbstract
Lung cancer has the highest morbidity and mortality rates worldwide. Pulmonary nodules are an early manifestation of lung cancer. Therefore, accurate classification of pulmonary nodules is of great significance for the early diagnosis and treatment of lung cancer. However, the classification of lung nodules is a complex and time-consuming task requiring extensive image reading and analysis by expert radiologists. Therefore, using deep learning technology to assist doctors in detecting and classifying pulmonary nodules has become a current research trend. A lightweight classification model named Res-VGG is proposed for classifying lung nodules as benign or malignant. The Res-VGG model improves on VGG16 by reducing the use of convolutional and fully connected layers. To reduce overfitting, residual connections are introduced. The training of the model was performed on the LUNA16 database, and a ten-fold cross-validation method was used to evaluate the performance of the model. In addition, the Res-VGG model was compared with three other common classification networks, and the results showed that the Res-VGG model outperformed the other models in terms of accuracy, sensitivity, and specificity.
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M. Marianna Sockrider, What is a Lung Nodule?, American Journal of Respiratory and Critical Care Medicine, 193 (2016) I.
Y. Xie, Y. Xia, J. Zhang, Y. Song, D. Feng, M. Fulham, W. Cai, Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT, IEEE transactions on medical imaging, 38 (2018) 991-1004.
S. Liu, Y. Xie, A. Jirapatnakul, A.P. Reeves, Pulmonary nodule classification in lung cancer screening with three-dimensional convolutional neural networks, Journal of Medical Imaging, 4 (2017) 041308-041308.
K.-L. Hua, C.-H. Hsu, S.C. Hidayati, W.-H. Cheng, Y.-J. Chen, Computer-aided classification of lung nodules on computed tomography images via deep learning technique, OncoTargets and therapy, (2015) 2015-2022.
W. Li, P. Cao, D. Zhao, J. Wang, Pulmonary nodule classification with deep convolutional neural networks on computed tomography images, Computational and mathematical methods in medicine, 2016 (2016).
P. Sahu, D. Yu, M. Dasari, F. Hou, H. Qin, A lightweight multi-section CNN for lung nodule classification and malignancy estimation, IEEE journal of biomedical and health informatics, 23 (2018) 960-968.
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
J. Gong, J. Liu, W. Hao, S. Nie, B. Zheng, S. Wang, W. Peng, A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images, European Radiology, 30 (2020) 1847-1855.
X. Xia, J. Gong, W. Hao, T. Yang, Y. Lin, S. Wang, W. Peng, Comparison and fusion of deep learning and radiomics features of ground-glass nodules to predict the invasiveness risk of stage-I lung adenocarcinomas in CT scan, Frontiers in oncology, 10 (2020) 418.
P. Wu, X. Sun, Z. Zhao, H. Wang, S. Pan, B. Schuller, Classification of lung nodules based on deep residual networks and migration learning, Computational intelligence and neuroscience, 2020 (2020).
X. Li, B. Hu, H. Li, B. You, Application of artificial intelligence in the diagnosis of multiple primary lung cancer, Thoracic cancer, 10 (2019) 2168-2174.
D.R. Baldwin, J. Gustafson, L. Pickup, C. Arteta, P. Novotny, J. Declerck, T. Kadir, C. Figueiras, A. Sterba, A. Exell, External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules, Thorax, 75 (2020) 306-312.
Y. Ren, M.-Y. Tsai, L. Chen, J. Wang, S. Li, Y. Liu, X. Jia, C. Shen, A manifold learning regularization approach to enhance 3D CT image-based lung nodule classification, International journal of computer assisted radiology and surgery, 15 (2020) 287-295.
S. Lakshmanaprabu, S.N. Mohanty, K. Shankar, N. Arunkumar, G. Ramirez, Optimal deep learning model for classification of lung cancer on CT images, Future Generation Computer Systems, 92 (2019) 374-382.
Q. Song, L. Zhao, X. Luo, X. Dou, Using deep learning for classification of lung nodules on computed tomography images, Journal of healthcare engineering, 2017 (2017).
V. Nair, G.E. Hinton, Rectified linear units improve restricted boltzmann machines, Proceedings of the 27th international conference on machine learning (ICML-10), 2010, pp. 807-814.
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556, (2014).
S.G. Armato III, G. McLennan, L. Bidaut, M.F. McNitt‐Gray, C.R. Meyer, A.P. Reeves, B. Zhao, D.R. Aberle, C.I. Henschke, E.A. Hoffman, The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans, Medical physics, 38 (2011) 915-931.
G. Zhang, Z. Yang, L. Gong, S. Jiang, L. Wang, H. Zhang, Classification of lung nodules based on CT images using squeeze-and-excitation network and aggregated residual transformations, La radiologia medica, 125 (2020) 374-383.
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