• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid
The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.
SCImago Journal & Country Rank

Journal Search

  1. Home
  2. Archive
  3. 2023-11 (Vol.72 No.11)
  4. 10.5370/KIEE.2023.72.11.1406
XML PDF INFO REF

References

1 
Ministry of Health and Welfare, 2022, Annual report of the National Cancer Registration Program 2020, Seoul: Ministry of Health and WelfareDOI
2 
Korea Consumer Agency, 2017, Cancer Misdiagnosis Consumer Damage Prevention Advisory, July. 13, 2017DOI
3 
Korea National Health Insurance Corporation, 2023, National cancer screening rate, 2023DOI
4 
P. Nanglia, S. Kumar, A. N. Mahajan, P. Singh, D. Rathee, 2021, A hybrid algorithm for lung cancer classification using SVM and Neural Networks, ICT Express, Vol. 7, No. 3, pp. 335-341DOI
5 
T. L. Chaunzwa, A. Hosny, Y. Xu, A. Shafer, N. Diao, M. Lanuti, D. C. Christiani, R. H. Mak, H. J. W. L. Aerts, 2021, Deep learning classification of lung cancer histology using CT images, Scientific reports, Vol. 11, No. 1, pp. 5471DOI
6 
A. Shimazaki, D. Ueda, A. Choppin, A. Yamamoto, T. Honjo, Y. Shimahara, Y. Miki, 2022, Deep learning-based algorithm for lung cancer detection on chest radiographs using the segmentation method, Scientific Reports, Vol. 12, No. 1, pp. 727DOI
7 
A. Teramoto, A. Yamada, Y. Kiriyama, T. Tsukamoto, K. Yan, L. Zhang, 2019, Automated classification of benign and malignant cells from lung cytological images using deep convolutional neural network, Informatics in Medicine Unlocked, Vol. 16, pp. 100205DOI
8 
H. F. Al-Yasriy, 2023, The IQ-OTH/NCCD lung cancer dataset, Mendeley Data, version 4DOI
9 
H. F. Al-Yasriy, M. S. Al-Husieny, F. Y. Mohsen, E. A. Khalil, Z. S. Hassan, 2020, Diagnosis of Lung Cancer Based on CT Scans Using CNN, IOP Conference Series: Materials Science and Engineering, Vol. 928DOI
10 
H. F. Kareem, M. S. A.-Husieny, F. Y. Mohsen, E. A. Khalil, Z. S. Hassan, 2021, Evaluation of SVM performance in the detection of lung cancer in marked CT scan dataset, Indonesian Journal of Electrical Engineering and Computer Science, Vol. 21, No. 3, pp. 1731-1738DOI
11 
E.D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, Q.V. Le, 2019, Autoaugment: Learning augmentation strategies from data. Proc, In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 113-123DOI
12 
A. Krizhevsky, 2009, Learning multiple layers of features from tiny images, Technical reportDOI
13 
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, 2009, Imagenet: A large-scale hierarchical image database, IEEE Conference on Computer Vision and Pattern Recognition, pp. 248-255DOI
14 
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, A. Y. Ng, 2011, Reading Digits in Natural Images with Unsupervised Feature Learning, Neural Information Processing Systems (NIPS)DOI
15 
M. Tan, Q. V. Le, 2019, EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks, International Conference on Machine LearningDOI
16 
T. Elsken, J. H. Metzen, F. Hutter, 2019, Neural Architecture Search: A Survey, Journal of Machine Learning Research, Vol. 20, pp. 1-21DOI
17 
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, 2017, MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, arXiv preprint arXiv:1704.04861DOI
18 
M. Tan, Q. V. Le, 2021, EfficientNetV2: Smaller Models and Faster Training, International Conference on Machine LearningDOI
19 
J. Hu, L. Shen, S. Albanie, G. Sun, E. Wu, 2018, Squeeze- and-Excitation Networks, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132-7141DOI
20 
Dosovitskiy, Beyer, Kolesnikov, Weissenborn, Zhai, Unterthiner, Dehghani, Minderer, Heigold, Gelly, Uszkoreit, Houlsby, 2020, An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, arXiv preprint arXiv:2010.11929DOI
21 
Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, S. Xie, Mar. 2022, A ConvNet for the 2020s, In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 11976-11986DOI
22 
S. Xie, R. Girshick, P. Dollár, Z. Tu, K. He, 2017, Aggregated residual transformations for deep neural networks, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1492-1500DOI