Automated Kidney Segmentation and Disease Classification Using CNN-Based Models
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-57624-9_3
Reference21 articles.
1. Zhu, X.-L., Shen, H.-B., Sun, H., Duan, L.-X., Xu, Y.-Y.: Improving segmentation and classification of renal tumors in small sample 3d ct images using transfer learning with convolutional neural networks. Int. J. Comput. Assist. Radiol. Surg. 17(7), 1303–1311 (2022)
2. Liu, J., Yildirim, O., Akin, O., Tian, Y.: Ai-driven robust kidney and renal mass segmentation and classification on 3d ct images. Bioengineering 10(1), 116 (2023)
3. Gharaibeh, M., et al.: Radiology imaging scans for early diagnosis of kidney tumors: a review of data analytics-based machine learning and deep learning approaches. Big Data Cogn. Comput. 6(1), 29 (2022)
4. Abdel-Fattah, M.A., Othman, N.A., Goher, N.: Predicting chronic kidney disease using hybrid machine learning based on apache spark. Comput. Intell. Neurosci. 2022 (2022)
5. Report, W.: The top 10 causes of death. WHO report, vol. 2020 (2020). https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
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