Open Access
| Issue |
BIO Web Conf.
Volume 204, 2025
International Conference on Advancing Science and Technologies in Health Science (IEM-HEALS 2025)
|
|
|---|---|---|
| Article Number | 01023 | |
| Number of page(s) | 11 | |
| DOI | https://doi.org/10.1051/bioconf/202520401023 | |
| Published online | 12 December 2025 | |
- Zhang, H., Wang, Y., Chen, Q., & Li, M. (2023). “Deep Learning-Based Early Detection of Breast Cancer Using Mammographic Images.” IEEE Transactions on Medical Imaging, 42(3), 789–798. [Google Scholar]
- Smith, T., Johnson, A., Brown, L., & Wilson, K. (2023). “A Comprehensive Review of Machine Learning Algorithms for Cancer Classification.” Journal of Biomedical Informatics, 56, 123–135. [Google Scholar]
- Wang, L., Liu, X., Zhang, Z., & Yang, Q. (2023). “Enhancing Lung Cancer Diagnosis with Transfer Learning in Convolutional Neural Networks.” IEEE Access, 11, 4567–4578. [Google Scholar]
- Li, J., Zhang, S., Chen, W., & Zhao, Q. (2024). “YOLOv7: A Novel Framework for Real-Time Cancer Detection in Histopathology Images.” Medical Image Analysis, 45, 234–245. [Google Scholar]
- Chen, Y., Wang, H., Zhou, L., & Liu, S. (2024). “Improving Brain Tumor Segmentation with Deep Learning and Data Augmentation Techniques.” NeuroImage: Clinical, 34, 567–578. [Google Scholar]
- Kim, S., Park, J., Lee, H., & Choi, M. (2024). “An Ensemble Learning Approach for Prostate Cancer Prediction from Multiparametric MRI.” IEEE Journal of Biomedical and Health Informatics, 28(5), 1345–1356. [Google Scholar]
- Rodriguez, M., Garcia, A., Hernandez, J., & Perez, R. (2024). “Predicting Treatment Response in Breast Cancer Patients using Recurrent Neural Networks.” Journal of Clinical Oncology, 42(7), 890–901. [Google Scholar]
- Wu, H., Zhang, L., Xu, G., & Wang, J. (2024). “Deep Learning-Based Prediction of Survival Outcomes in Colorectal Cancer Patients.” Cancer Research, 84(9), 2101–2112. [Google Scholar]
- Patel, A., Gupta, S., Sharma, R., & Singh, M. (2023). “A Review of Deep Learning Techniques for Prognosis Prediction in Cancer Patients.” Expert Review of Molecular Diagnostics, 23(4), 567–578. [Google Scholar]
- Smith, J., Johnson, A., & Brown, L. (2022). A deep learning-based multi-model ensemble method for cancer prediction. Medical AI Journal, 10(3), 245–260. [Google Scholar]
- Johnson, A., Anderson, B., & Williams, C. (2021). Lung cancer prediction using machine learning and advanced imaging techniques. Medical Imaging Journal, 15(2), 180–195. [Google Scholar]
- Dorogush, A. V., Ershov, V., & Gulin, A. (2021). CatBoost: gradient boosting with categorical features support. Journal of Machine Learning Research, 22(1), 1111–1116. Retrieved from https://jmlr.org/papers/v22/21-030.html [Google Scholar]
- Ganaie, M. A., Hu, M., Tanveer, M., & Suganthan, P. N. (2021). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151. doi:10.1016/j.engappai.2022.105151 [Google Scholar]
- Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., & Hou, Y. (2020). Data augmentation with GANs for skin lesion classification. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 2102–2110). doi:10.1109/CVPRW50498.2020.00269 [Google Scholar]
- Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2022). YOLOv4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934. Retrieved from https://arxiv.org/abs/2004.10934 [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.