The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images
Open accessAbstract
Lung cancer is the most common cancer that cannot be ignored and cause death with late health care. Currently, CT can be used to help doctors detect the lung cancer in the early stages. In many cases, the diagnosis of identifying the lung cancer depends on the experience of doctors, which may ignore some patients and cause some problems. Deep learning has been proved as a popular and powerful method in many medical imaging diagnosis areas. In this paper, three types of deep neural networks (e.g., CNN, DNN, and SAE) are designed for lung cancer calcification. Those networks are applied to the CT image classification task with some modification for the benign and malignant lung nodules. Those networks were evaluated on the LIDC-IDRI database. The experimental results show that the CNN network archived the best performance with an accuracy of 84.15%, sensitivity of 83.96%, and specificity of 84.32%, which has the best result among the three networks.
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Cites
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759
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Ghaffar Nia, N., Kaplanoglu, E., & Nasab, A. (2023) · Discover Artificial Intelligence · doi:10.1007/s44163-023-00049-5 · Cited by 426
Hybrid deep learning for detecting lung diseases from X-ray images
Bharati, S., Podder, P., & Mondal, M. R. H. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100391 · Cited by 404
Deep learning for lung Cancer detection and classification
Asuntha, A., & Srinivasan, A. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08394-3 · Cited by 382
Deep learning with convolutional neural network in radiology
Yasaka, K., Akai, H., Kunimatsu, A., Kiryu, S., & Abe, O. (2018) · Japanese Journal of Radiology · doi:10.1007/s11604-018-0726-3 · Cited by 368
Transfer learning techniques for medical image analysis: A review
Kora, P., Ooi, C. P., Faust, O., Raghavendra, U., Gudigar, A., Chan, W. Y., Meenakshi, K., Swaraja, K., Plawiak, P., & Rajendra Acharya, U. (2022) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2021.11.004 · Cited by 367
Metadata
- Authors
- Song, Q., Zhao, L., Luo, X., & Dou, X.
- Venue
- Journal of Healthcare Engineering
- Publisher
- Wiley
- Published
- 2017
- Volume
- 2017
- Pages
- 1-7
- ISSN
- 2040-2295 (ISSN-L)
- 2040-2309
- OpenAlex
- W2743008510
- Cited by
- 422
- References
- 2
- FWCI
- 18.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
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