Urine Sediment Analysis by Using Convolution Neural Network
Author:
Affiliation:
1. Erbil Polytechnic University,Techniques Erbil Technical Engineering College,Department of Information System Engineering,Erbil,Iraq
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9807426/9807452/09807482.pdf?arnumber=9807482
Reference24 articles.
1. An Accurate Urine Erythrocytes Detection Model Coupled Faster RCNN with VggNet
2. Automatic Urinary Sediments Visible Component Detection Based on Improved YOLO Algorithm
3. Cell Detection and Classification from Urine Sediment Microscopic Images;goswami,0
4. The image-based analysis and classification of urine sediments using a LeNet-5 neural network
5. Ensemble of fine‐tuned convolutional neural networks for urine sediment microscopic image classification
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1. An Optimized Data and Model Centric Approach for Multi-Class Automated Urine Sediment Classification;IEEE Access;2024
2. Improvement of YOLOv7 with Attention Modules for Urinary Sediment Particle Detection;2023 IEEE Biomedical Circuits and Systems Conference (BioCAS);2023-10-19
3. Automated detection of multi-class urinary sediment particles: An accurate deep learning approach;Biocybernetics and Biomedical Engineering;2023-10
4. Swin-LBP: a competitive feature engineering model for urine sediment classification;Neural Computing and Applications;2023-08-18
5. Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model;Diagnostics;2023-03-30
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