A Computer Assisted Detection Framework of Kidney Diseases Based on CNN Model
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-36246-0_21
Reference26 articles.
1. Zhang, H., Botler, M., Kooman, J.P.: Deep learning for image analysis in kidney care. ScienceDirect J. Adv. Kidney Dis. Health 30(1), 25–32 (2023). https://doi.org/10.1053/j.akdh.2022.11.003
2. Alzu’bi, D., Abdullah, M., Hmeidi, I., AlAzab, R., Gharaibeh, M., El-Heis, M., Almotairi, K.H., Forestiero, A., Hussein, A.M., Abualigah, L.: Kidney tumor detection and classification based on deep learning approaches: a new dataset in CT scans. Hindawi J. Healthcare Eng. 2022, 22. https://doi.org/10.1155/2022/3861161
3. Dritsas, E., Trigka, M.: Machine learning techniques for chronic kidney disease risk prediction. Big Data Cogn. Comput. 6(3), 98. Received 29 June 2022. Revised 25 Aug 2022. Accepted 8 Sept 2022. Published 14 Sept 2022. https://doi.org/10.3390/bdcc6030098
4. Farzaneh, N., Reza Soroushmehr, S.M., Patel, H., Wood, A., Gryak, J., Fessell, D., Najarian, K.: Automated kidney segmentation for traumatic injured patients through ensemble learning and active contour modeling. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2018, 3418–3421. https://doi.org/10.1109/EMBC.2018.8512967
5. Gharaibeh, S.M., Alzu’bi, D., Abdullah, M., Hmeidi, I., Nasar, M.R.A., Abualigah, L., Gandomi, A.H.: 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, 29. https://doi.org/10.3390/bdcc6010029, Received 8 Feb 2022. Accepted 4 Mar 2022. Published 8 Mar 2022. https://www.mdpi.com/journal/bdcc
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