Classification of Pulmonary CT Images by Using Hybrid 3D-Deep Convolutional Neural Network Architecture
Source abstract
Abstract
Lung cancer is the most common cause of cancer-related deaths worldwide. Hence, the survival rate of patients can be increased by early diagnosis. Recently, machine learning methods on Computed Tomography (CT) images have been used in the diagnosis of lung cancer to accelerate the diagnosis process and assist physicians. However, in conventional machine learning techniques, using handcrafted feature extraction methods on CT images are complicated processes. Hence, deep learning as an effective area of machine learning methods by using automatic feature extraction methods could minimize the process of feature extraction. In this study, two Convolutional Neural Network (CNN)-based models were proposed as deep learning methods to diagnose lung cancer on lung CT images. To investigate the performance of the two proposed models (Straight 3D-CNN with conventional softmax and hybrid 3D-CNN with Radial Basis Function (RBF)-based SVM), the altered models of two-well known CNN architectures (3D-AlexNet and 3D-GoogleNet) were considered. Experimental results showed that the performance of the two proposed models surpassed 3D-AlexNet and 3D-GoogleNet. Furthermore, the proposed hybrid 3D-CNN with SVM achieved more satisfying results (91.81%, 88.53% and 91.91% for accuracy rate, sensitivity and precision respectively) compared to straight 3D-CNN with softmax in the diagnosis of lung cancer.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Lung Cancer Diagnosis and Treatment · Radiomics and Machine Learning in Medical Imaging · COVID-19 diagnosis using AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Engineering, Faculty of Technology, Gazi University, 06500 Ankara, Turkey
References (39)
Show source-provided references
1. Computer aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy10.1186/s12938-015-0120-7 ↗
2. Wang, M., Long, F., Tang, F., Jing, Y., Wang, X., Yao, L., Ma, J., Fei, Y., Chen, L., and Wang, G. (2017). Autofluorescence Imaging and Spectroscopy of Human Lung Cancer. Appl. Sci., 7.10.3390/app7010032 ↗
3. Annual Report to the Nation on the Status of Cancer, 1973–1996, With a Special Section on Lung Cancer and Tobacco Smoking10.1093/jnci/91.8.675 ↗
4. Kulkarni, A., and Panditrao, A. (2014, January 8–10). Classification of lung cancer stages on CT scan images using image processing. Proceedings of the IEEE International Conference on Advanced Communications, Control and Computing Technologies, Ramanathapuram, India.10.1109/ICACCCT.2014.7019327 ↗
5. Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans10.1038/srep24454 ↗
6. Geng, M., Wang, Y., Tian, Y., and Huang, T. (2016, January 20–22). CNUSVM: Hybrid CNN-Uneven SVM Model for Imbalanced Visual Learning. Proceedings of the IEEE Second International Conference on Multimedia Big Data, Taipei, Taiwan.10.1109/BigMM.2016.19 ↗
7. Cascio, D., Taormina, V., and Raso, G. (2019). Deep Convolutional Neural Network for HEp-2 Fluorescence Intensity Classification. Appl. Sci., 9.10.3390/app9030408 ↗
8. Sun, W., Zheng, B., and Qian, W. (2016, January 24). Computer aided lung cancer diagnosis with deep learning algorithms. Proceedings of the International Society for Optics and Photonics Conference, San Diego, CA, USA.10.1117/12.2216307 ↗
9. Ginneken, B.V., Setio, A.A.A., Jacobs, C., and Ciompi, F. (2015, January 16–19). Off-The-Shelf Convolutional Neural Network Features for Pulmonary Nodule Detection in Computed Tomography Scans. Proceedings of the IEEE 12th International Symposium on Biomedical Imaging (ISBI), New York, NY, USA.
10. Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network10.1109/TMI.2016.2535865 ↗
11. Gruetzemacher, R., and Gupta, A. (2016, January 11). Using Deep Learning for Pulmonary Nodule Detection & Diagnosis. Proceedings of the Twenty-Second Americas Conference on Information Systems, San Diego, CA, USA.
12. Towards automatic pulmonary nodule management in lung cancer screening with deep learning10.1038/srep46479 ↗
13. Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D.D., and Chen, M. (2014, January 10–12). Medical Image Classification with Convolutional Neural Network. Proceedings of the 13th International Conference on Control, Automation, Robotics & Vision, Marina Bay Sands, Singapore.10.1109/ICARCV.2014.7064414 ↗
14. Multi-scale Convolutional Neural Networks for Lung Nodule Classification
15. Rao, P., Pereira, N.A., and Srinivasan, R. (2016, January 14–17). Convolutional Neural Networks for Lung Cancer Screening in Computed Tomography (CT) Scans. Proceedings of the 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida, India.10.1109/IC3I.2016.7918014 ↗
16. Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images10.1155/2017/8314740 ↗
17. Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network
18. (2019, February 05). Data Science Bowl 2017. Available online: https://www.kaggle.com/c/data-science-bowl-2017.
19. Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)10.14569/IJACSA.2017.080853 ↗
20. Kuan, K., Ravaut, M., Manek, G., Chen, H., Lin, J., Nazir, B., Chen, C., Howe, T.C., Zeng, Z., and Chandrasekhar, V. (arXiv, 2017). Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge, arXiv.
21. Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network
22. Serj, M.F., Lavi, B., Hoff, G., and Valls, D.P. (arXiv, 2018). A Deep Convolutional Neural Network for Lung Cancer Diagnostic, arXiv.
23. Jin, T., Cui, H., Zeng, S., and Wang, X. (December, January 29). Learning deep spatial lung features by 3D convolutional neural network for early cancer detection. Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA), Sydney, Australia.
24. Deep learning10.1038/nature14539 ↗
25. Laszlo, E., Szolgay, P., and Nagy, Z. (2012, January 29–31). Analysis of a GPU based CNN implementation. Proceedings of the 13th International Workshop on Cellular Nanoscale Networks and their Applications, Turin, Italy.10.1109/CNNA.2012.6331451 ↗
26. A fast learning algorithm for deep belief nets10.1162/neco.2006.18.7.1527 ↗
27. Receptive Fields and Functional Architecture of Monkey Striate Cortex10.1113/jphysiol.1968.sp008455 ↗
28. Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation10.1162/neco.2009.10-08-881 ↗
29. Szegedy, C., Liu, W., Jia, W., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (arXiv, 2014). Going deeper with convolutions, arXiv.10.1109/CVPR.2015.7298594 ↗
30. (2019, February 04). Image Net. Available online: http://www.webcitation.org/query?url=http%3A%2F%2Fimage-net.org%2F&date=2017-12-27.
31. The Complex Backpropagation Algorithm10.1109/78.134446 ↗
32. Scherer, D., Müller, A., and Behnke, S. (2010, January 15–18). Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition. Proceedings of the 20th International Conference on Artificial Neural Networks (ICANN), Thessaloniki, Greece.10.1007/978-3-642-15825-4_10 ↗
33. Maas, A.L., Hannun, A.Y., and Ng, A.Y. (2013, January 16–21). Rectifier nonlinearities improve neural network acoustic models. Proceedings of the 30th International Conference on Machine Learning, Atlanta, GA, USA.
34. Kang, X., Song, B., and Sun, F. (2019). A Deep Similarity Metric Method Based on Incomplete Data for Traffic Anomaly Detection in IoT. Appl. Sci., 9.10.3390/app9010135 ↗
35. Bouchard, G. (2008, January 31). Efficient Bounds for the Softmax Function and Applications to Approximate Inference in Hybrid models. Proceedings of the Presentation at the Workshop For Approximate Bayesian Inference in Continuous/Hybrid Systems at Neural Information Processing Systems (NIPS), Meylan, France.
36. Breast cancer detection from FNA using SVM with different parameter tuning systems and SOM–RBF classifier10.1016/j.jfranklin.2006.09.005 ↗
37. Suapang, P., Dejhan, K., and Yimmun, S. (2009, January 18–21). Medical Image Compression and DICOM-Format Image Archive. Proceedings of the ICROS-SICE International Joint Conference, Fukuoka, Japan.
38. Usefulness of hounsfield unit and density in the assessment and treatment of urinary stones
39. Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3–6). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Advances in Neural Information Processing Systems 25 (NIPS 2012), Lake Tahoe, NV, USA.