An Image Recognition Method for Urine Sediment Based on Semi-supervised Learning

Ji, Qingbo; Jiang, Yue; Wu, Zhangwei; Liu, Qingquan; Qu, Lijun

doi:10.1016/j.irbm.2022.09.006

Cited by 3 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CNN, R‐CNN, FAST R‐CNN, FASTER R‐CNN, and YOLO family of cutting‐edge technology methods that achieve high success in object detection to overcome these problems. Ji et al proposed a reparameterization network (US‐RepNet) that can recognize 16 different particles and has a classification accuracy of 94% 4 . In another study, Ji et al proposed a structure consisting of 3 CNN modules with 97% accuracy for the recognition of 10 different particles.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Çınar,

Erkuş,

Tuncer

et al. 2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Ji et al proposed a reparameterization network (US-RepNet) that can recognize 16 different particles and has a classification accuracy of 94%. 4 In another study, Ji et al proposed a structure consisting of 3 CNN modules with 97% accuracy for the recognition of 10 different particles. The first module distinguishes 10 particles.…”

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

View full text Add to dashboard Cite

show abstract

“…During the experiments, a data set containing 429,605 urine sediment images with 16 classes was used. They stated that they obtained a 94% accuracy value with the model called US-RepNet that they suggested [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model

et al. 2023

View full text Add to dashboard Cite

Urine sediment examination is one of the main tests used in the diagnosis of many diseases. Thanks to this test, many diseases can be detected in advance. Examining the results of this test is an intensive and time-consuming process. Therefore, it is very important to automatically interpret the urine sediment test results using computer-aided systems. In this study, a data set consisting of eight classes was used. The data set used in the study consists of 8509 particle images obtained by examining the particles in the urine sediment. A hybrid model based on textural and Convolutional Neural Networks (CNN) was developed to classify the images in the related data set. The features obtained using textural-based methods and the features obtained from CNN-based architectures were combined after optimizing using the Minimum Redundancy Maximum Relevance (mRMR) method. In this way, we aimed to extract different features of the same image. This increased the performance of the proposed model. The CNN-based ResNet50 architecture and textural-based Local Binary Pattern (LBP) method were used for feature extraction. Finally, the optimized and combined feature map was classified at different machine learning classifiers. In order to compare the performance of the model proposed in the study, results were also obtained from different CNN architectures. A high accuracy value of 96.0% was obtained in the proposed model.

show abstract

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

Akhtar,

Hanif,

Rashid

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

An Image Recognition Method for Urine Sediment Based on Semi-supervised Learning

Cited by 3 publications

References 31 publications

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

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

Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model

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

Contact Info

Product

Resources

About