Classification of Lung Nodule Using Hybridized Deep Feature Technique

Document Type: Special Issue: The Importance of Human Computer Interaction: Challenges, Methods and Applications


1 Assistant Prof., Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore – 641114, Tamil Nadu, India.

2 Computer Vision Intern, Vasundharaa Geo Technologies, Pune, Maharashtra, India.


Deep learning techniques have become very popular among Artificial Intelligence (AI) techniques in many areas of life. Among many types of deep learning techniques, Convolutional Neural Networks (CNN) can be useful in image classification applications. In this work, a hybridized approach has been followed to classify lung nodule as benign or malignant. This will help in early detection of lung cancer and help in the life expectancy of lung cancer patients thereby reducing the mortality rate by this deadly disease scourging the world. The hybridization has been carried out between handcrafted features and deep features. The machine learning algorithms such as SVM and Logistic Regression have been used to classify the nodules based on the features. The dimensionality reduction technique, Principle Component Analysis (PCA) has been introduced to improve the performance of hybridized features with SVM. The experiments have been carried out with 14 different methods. It has been found that GLCM + VGG19 + PCA + SVM outperformed all other models with an accuracy of 94.93%, sensitivity of 90.9%, specificity of 97.36% and precision of 95.44%. The F1 score was found to be 0.93 and the AUC was 0.9843. The False Positive Rate was found to be 2.637% and False Negative Rate was 9.09%.


Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2(4), 433-459.
Armato III, S. G., McLennan, G., Bidaut, L., McNitt‐Gray, M. F., Meyer, C. R., Reeves, A. P., ... & Kazerooni, E. A. (2011). 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(2), 915-931.
Balaji, K., & Lavanya, K. (2018). Recent Trends in Deep Learning with Applications. In Cognitive Computing for Big Data Systems Over IoT (pp. 201-222). Springer, Cham
da Nóbrega, R. V. M., Peixoto, S. A., da Silva, S. P. P., & Rebouças Filho, P. P. (2018, June). Lung nodule classification via deep transfer learning in CT lung images. In 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS) (pp. 244-249). IEEE
de Carvalho Filho, A. O., Silva, A. C., de Paiva, A. C., Nunes, R. A., & Gattass, M. (2018). Classification of patterns of benignity and malignancy based on CT using topology-based phylogenetic diversity index and convolutional neural network. Pattern Recognition, 81, 200-212.
Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009, June). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248-255). IEEE.
Dhara, A. K., Mukhopadhyay, S., Dutta, A., Garg, M., & Khandelwal, N. (2016). A combination of shape and texture features for classification of pulmonary nodules in lung CT images. Journal of digital imaging, 29(4), 466-475.
Fukushima, K. (1980). Biological cybernetics neocognitron: a self‐organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern, 36, 193-202.
Han, F., Wang, H., Zhang, G., Han, H., Song, B., Li, L., ... & Liang, Z. (2015). Texture feature analysis for computer-aided diagnosis on pulmonary nodules. Journal of digital imaging, 28(1), 99-115.
Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, (6), 610-621.
Hua, K. L., Hsu, C. H., Hidayati, S. C., Cheng, W. H., & Chen, Y. J. (2015). Computer-aided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets and therapy, 8, 2015-2022.
Hussein, S., Cao, K., Song, Q., & Bagci, U. (2017, June). Risk stratification of lung nodules using 3D CNN-based multi-task learning. In International conference on information processing in medical imaging (pp. 249-260). Springer, Cham.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
Kumar, D., Wong, A., & Clausi, D. A. (2015, June). Lung nodule classification using deep features in CT images. In 2015 12th Conference on Computer and Robot Vision (pp. 133-138). IEEE.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324
Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (Eds.), (2020) Communications, Signal Processing, and Systems: Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems, Springer.
Lo, S. C. B., Chan, H. P., Lin, J. S., Li, H., Freedman, M. T., & Mun, S. K. (1995). Artificial convolution neural network for medical image pattern recognition. Neural networks, 8(7-8), 1201-1214.
Ma, Y., Xie, Q., Liu, Y., & Xiong, S. (2019). A weighted KNN-based automatic image annotation method. Neural Computing and Applications, 1-12
Mastouri, R., Khlifa, N., Neji, H., & Hantous-Zannad, S. (2020). Deep learning-based CAD schemes for the detection and classification of lung nodules from CT images: A survey. Journal of X-Ray Science and Technology, (Preprint), 1-27
Olivas, E. S., Guerrero, J. D. M., Martinez-Sober, M., Magdalena-Benedito, J. R., & Serrano, L. (Eds.). (2009). Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques: Algorithms, Methods, and Techniques. IGI Global.
Ourselin, S., Joskowicz, L., Sabuncu, M. R., Unal, G., & Wells, W. (Eds.). (2016). Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II (Vol. 9901). Springer.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., ... & Berg, A. C. (2015). Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3), 211-252.
Setio, A. A. A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., Van Riel, S. J., ... & van Ginneken, B. (2016). Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE transactions on medical imaging, 35(5), 1160-1169.
Shen, W., Zhou, M., Yang, F., Yang, C., & Tian, J. (2015, June). Multi-scale convolutional neural networks for lung nodule classification. In International Conference on Information Processing in Medical Imaging (pp. 588-599). Springer, Cham.
Shen, W., Zhou, M., Yang, F., Yu, D., Dong, D., Yang, C., ... & Tian, J. (2017). Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognition, 61, 663-673.
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Tajbakhsh, N., & Suzuki, K. (2017). Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs. Pattern recognition, 63, 476-486.
Wang, H., Zhao, T., Li, L. C., Pan, H., Liu, W., Gao, H., ... & Liang, Z. (2018). A hybrid CNN feature model for pulmonary nodule malignancy risk differentiation. Journal of X-ray Science and Technology, 26(2), 171-187.
Wani, M. A., Bhat, F. A., Afzal, S., & Khan, A. I. (2020). Advances in deep learning (Vol. 57). Berlin: Springer.
Wei, G., Cao, H., Ma, H., Qi, S., Qian, W., & Ma, Z. (2018). Content-based image retrieval for lung nodule classification using texture features and learned distance metric. Journal of medical systems, 42(1), 13.
Wei, G., Ma, H., Qian, W., Han, F., Jiang, H., Qi, S., & Qiu, M. (2018). Lung nodule classification using local kernel regression models with out-of-sample extension. Biomedical Signal Processing and Control, 40, 1-9.
Weng, J. J., Ahuja, N., & Huang, T. S. (1993, May). Learning recognition and segmentation of 3-D objects from 2-D images. In 1993 (4th) International Conference on Computer Vision (pp. 121-128). IEEE.
Zhu, W., Liu, C., Fan, W., & Xie, X. (2018, March). Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 673-681). IEEE.