Open Access
Issue |
E3S Web of Conf.
Volume 465, 2023
8th International Conference on Industrial, Mechanical, Electrical and Chemical Engineering (ICIMECE 2023)
|
|
---|---|---|
Article Number | 02053 | |
Number of page(s) | 6 | |
Section | Symposium on Electrical, Information Technology, and Industrial Engineering | |
DOI | https://doi.org/10.1051/e3sconf/202346502053 | |
Published online | 18 December 2023 |
- B. Weigelt, F. C. Geyer, and J. S. Reis-Filho, “Histological types of breast cancer: How special are they?” Molecular Oncology, vol. 4, no. pp. 192-208, 2010. [Online]. Available: https://doi.org/10.1016/j.molonc.2010.04.004 [CrossRef] [PubMed] [Google Scholar]
- D. Saslow, J. Hannan, J. Osuch, M. H. Alciati, C. Baines, M. Barton, et al., “Clinical breast examination: practical recommendations for optimizing performance and reporting,” CA: A Cancer Journal for Clinicians, vol. 54, no. 6, pp. 327-344, 2004. [Online]. Available: https://doi.org/10.3322/canjclin.54.6.327 [CrossRef] [PubMed] [Google Scholar]
- E. A. Mohamed, E. A. Rashed, T. Gaber, and O. Karam, “Deep learning model for fully automated breast cancer detection system from thermograms,” PLOS ONE, vol. 17, no. 1, e0262349, 2022. [Online]. Available: https://doi.org/10.1371/journal.pone.0262349 [CrossRef] [PubMed] [Google Scholar]
- J. Zuluaga-Gomez, Z. al Masry, K. Benaggoune, S. Meraghni, and N. Zerhouni, “A CNN-based methodology for breast cancer diagnosis using thermal images,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol. 9, no. 2, pp. 131-145, 2021. [Online]. Available: https://doi.org/10.1080/21681163.2020.1824685 [CrossRef] [Google Scholar]
- S. Arooj et al., “Breast Cancer Detection and Classification Empowered With Transfer Learning,” Frontiers in Public Health, vol. 10, 2022. [Online]. Available: https://doi.org/10.3389/fpubh.2022.924432 [CrossRef] [Google Scholar]
- D. Opitz and R. Maclin, “Popular Ensemble Methods: An Empirical Study,” Journal of Artificial Intelligence Research, vol. 11, pp. 169-198, 1999. [Online]. Available: https://doi.org/10.1613/jair.614 [CrossRef] [Google Scholar]
- A. Das, “Adaptive UNet-based Lung Segmentation and Ensemble Learning with CNN-based Deep Features for Automated COVID-19 Diagnosis,” Multimedia Tools and Applications, vol. 81, no. 4, pp. 54075441, 2022. DOI: 10.1007/s11042-021-11787-y. [Google Scholar]
- A. Yazdizadeh, Z. Patterson, and B. Farooq, “Ensemble Convolutional Neural Networks for Mode Inference in Smartphone Travel Survey,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 6, pp. 2232-2239, 2020. DOI: 10.1109/TITS.2019.2918923. [CrossRef] [Google Scholar]
- A. Altameem, C. Mahanty, R. C. Poonia, A. K. J. Saudagar, and R. Kumar, “Breast Cancer Detection in Mammography Images Using Deep Convolutional Neural Networks and Fuzzy Ensemble Modeling Techniques,” Diagnostics, vol. 12, no. 8, p. 1812, 2022. DOI: 10.3390/diagnostics12081812. [CrossRef] [PubMed] [Google Scholar]
- J. Suckling, “The Mammographic Image Analysis Society Digital Mammogram Database,” Cambridge Repository. [Google Scholar]
- R. S. Lee, F. Gimenez, A. Hoogi, and D. Rubin, “Curated Breast Imaging Subset of DDSM,” The Cancer Imaging Archive. [Google Scholar]
- I. C. Moreira, I. Amaral, I. Domingues, A. Cardoso, M. J. Cardoso, and J. S. Cardoso, “INbreast,” Academic Radiology, vol. 19, no. 2, pp. 236248, 2012. DOI: 10.1016/j.acra.2011.09.014. [CrossRef] [PubMed] [Google Scholar]
- K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint arXiv:1409.1556, 2014. [Google Scholar]
- K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778. DOI: 10.1109/CVPR.2016.90. [Google Scholar]
- C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, “Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31, no. 1, 2017. DOI: 10.1609/aaai.v31i1.11231. [CrossRef] [Google Scholar]
- R. Wang, “AdaBoost for Feature Selection, Classification and Its Relation with SVM, A Review,” Physics Procedia, vol. 25, pp. 800-807, 2012. DOI: 10.1016/j.phpro.2012.03.160. [CrossRef] [Google Scholar]
- A. Canziani, A. Paszke, and E. Culurciello, “An Analysis of Deep Neural Network Models for Practical Applications.” [Google Scholar]
- G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks.” [Google Scholar]
- J. Chen, S. Shan, C. He, G. Zhao, M. Pietikäinen, X. Chen, and W. Gao, “WLD: A Robust Local Image Descriptor,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1705– 1720, 2010. DOI: 10.1109/TPAMI.2009.155. [CrossRef] [PubMed] [Google Scholar]
- Z.-H. Zhou, “Ensemble Methods.” Chapman and Hall/CRC, 2012. DOI: 10.1201/b12207. [CrossRef] [Google Scholar]
- A. W. Salehi, S. Khan, G. Gupta, B. I. Alabduallah, A. Almjally, H. Alsolai, T. Siddiqui, and A. Mellit, “A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope,” Sustainability, vol. 15, no. 7, p. 5930, 2023. DOI: 10.3390/su15075930. [CrossRef] [Google Scholar]
- L. I. Kuncheva and C. J. Whitaker, “Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy,” Machine Learning, vol. 51, no. 2, pp. 181–207, 2003. DOI: 10.1023/A:1022859003006. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.