Hybrid classification framework for chronic kidney disease prediction model-Reference-Cited by-同舟云学术

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Hybrid classification framework for chronic kidney disease prediction model

Published:2023-06-22 Issue: Volume: Page:1-15
ISSN:1368-2199
Container-title:The Imaging Science Journal
language:en
Short-container-title:The Imaging Science Journal

Author:

Patil Smitha¹,Choudhary Savita²

Affiliation:

1. Department of Computer Science and Engineering, Sir M Visvesvaraya Institute of Technology, Bengaluru Presidency University, Bengaluru, India

2. Department of Computer Science and Engineering, Sir M Visvesvaraya Institute of Technology, Bengaluru, India

Publisher

Informa UK Limited

Subject

Computer Vision and Pattern Recognition,Media Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/13682199.2023.2206272

Reference48 articles.

1. A comparison of ultrasound and magnetic resonance imaging shows that kidney length predicts chronic kidney disease in autosomal dominant polycystic kidney disease

2. Cardiovascular risk prediction in chronic kidney disease patients

3. Prediction of chronic kidney disease using urinary dielectric properties and support vector machine

4. Ultrasound super-resolution imaging provides a noninvasive assessment of renal microvasculature changes during mouse acute kidney injury

5. The predictive value of sonographic placental markers for adverse pregnancy outcome in women with chronic kidney disease

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1. Classification of Images Related to Kidney Cancer using Hybrid Deep Learning;2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE);2024-01-24

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