Urine Calcium Oxalate Crystallization Recognition Method Based on Deep Learning

Xiang, Huagou; Chen, Qiwen; Wu, Yajie; Xu, Danfeng; Qi, Suwen; Mei, Jun; Li, Qiaoliang; Liu, Xinyu

doi:10.1109/icactm.2019.8776769

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…[26], [27], they achieved 96% accuracy for an eight-class problem. Research works [28], [29] and [30] employed feature extraction involving ResNet50, local binary pattern, and machine learning classifiers for classification. In 2022, Q. Ji et al [18] presented a semi-supervised learning method using US-RepNet for complex feature extraction from low-resolution urine sediment images.…”

Section: B Contributions/noveltiesmentioning

confidence: 99%

An Optimized Data and Model Centric Approach for Multi-Class Automated Urine Sediment Classification

Akhtar,

Hanif,

Rashid

et al. 2024

IEEE Access

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Automated urine sediment analyzers play a crucial role in diagnosing urinary tract infections, offering real-time data analysis and expediting patient diagnosis. This paper introduces a novel hybrid approach combining data-centric and model-centric techniques for automated urine sediment analysis. The proposed methodology addresses challenges such as morphological similarities among particle classes, uneven particle distribution, and intra/inter-class variations. A modified version of convolutional neural network (CNN), specifically the Visual Geometry Group (VGG-19) model, incorporating transfer learning, along with data augmentation is proposed for automated urine sediment classification with 98% accuracy and impressive inference time of 61ms per image. The proposed approach outperforms existing methods, especially in handling diverse sediment categories, demonstrating its potential for practical applications in medical diagnostics. We proposed the integration of a data-centric approach for improved labeling reliability and a model-centric approach for fine-tuning of the deep learning model, showcasing promising results in recognizing 12 distinct urine sediment classes. This study also emphasizes the importance of collaboration with medical professionals in refining the model's performance and handling challenges related to data acquisition and class imbalance. The proposed approach provides a significant advancement in automating and enhancing urine sediment analysis processes.

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Section: B Contributions/noveltiesmentioning

confidence: 99%

An Optimized Data and Model Centric Approach for Multi-Class Automated Urine Sediment Classification

Akhtar,

Hanif,

Rashid

et al. 2024

IEEE Access

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“…Average precision of 84.1% was obtained for erythrocyte, crystal, leukocyte, epithelial, mycete, cast, and epithelial cells 7 . In Reference 8, which focused only on the detection of Calcium Oxalate Crystal, 74% accuracy was achieved with the Resnet50 model. Li et al classified urine sample images based on shape analysis using an improved LeNet‐5 neural network 9 …”

Section: Related Workmentioning

confidence: 99%

YOLOv5 based detector for eight different urine particles components on single board computer

Çınar,

Erkuş,

Tuncer

et al. 2023

Int J Imaging Syst Tech

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Urine sediment examination (USE) is an important issue in the detection of urinary system diseases and is a prerequisite, especially in the diagnosis of kidney diseases. The low‐contrast urine sediment images contain many particles, and these particles are superimposed or adherent, making it difficult to detect and interpret the particles. In this paper, a YOLOv5‐based urine analysis system, which is one of the fastest and most successful architectures used for object detection, is presented to identify particles in urine. The artificial intelligence‐based system, which can recognize bacteria, leukocytes, erythrocytes, crystals, yeast, cylinders, epithelium, and other particles, provides counting and reporting of components in the images obtained from the centrifuged urine sample through a microscope. The system consists of taking images from the microscope and recording them on SBC (Single Board Computer), artificial intelligence‐based software running on SBC, and database systems where urine analysis results are recorded. Different YOLOv5x architectures were used to evaluate the performance of the system and Precision, Recall, mAP, and F1_score values were obtained as metrics. According to the results obtained with the YOLOv5n, YOLOv5s, and YOLOv5m architectures, the highest mAP value of the system, which can recognize eight different particles, was 95.8% with YOLOv5m. This artificial intelligence‐based system, which will help laboratory workers, will not only save time but also eliminate the standardization differences in manual microscopy and will provide benefits as full‐time educational material.

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“…[ 64 ] Risk for stone disease Case-control Accuracy of 89% Accuracy of 74% Xiang et al. [ 65 ] Identification of CaOx crystallization in urine sediment Cross-sectional Accuracy of 74% Accuracy of 74% Kletzmayr et al. [ 66 ] Recognition of crystallization inhibition Experimental IP6 analogues inhibit effectively CaOx crystallization No comparator Kriegshauser et al.…”

Section: Ai For the Elucidation Of Stone Disease Chemistry And Compos...mentioning

confidence: 99%

“…Using microscopy images and an identification procedure based on a convolution neural network, another study reported an improved rate (74%) of recognition of CaOx crystals in urine sediment, which are considered as a predictive factor for developing CaOx stones, compared to the standard identification procedure [ 65 ].…”

Section: Ai For the Elucidation Of Stone Disease Chemistry And Compos...mentioning

confidence: 99%

Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review

Anastasiadis

Koudonas

Langas

et al. 2023

Asian Journal of Urology

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Urine Calcium Oxalate Crystallization Recognition Method Based on Deep Learning

Cited by 7 publications

References 9 publications

An Optimized Data and Model Centric Approach for Multi-Class Automated Urine Sediment Classification

An Optimized Data and Model Centric Approach for Multi-Class Automated Urine Sediment Classification

YOLOv5 based detector for eight different urine particles components on single board computer

Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review

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