Research on Urine Sediment Images Recognition Based on Deep Learning

Ji, Qingbo; Li, Xun; Qu, Zhiyu; Dai, Chong

doi:10.1109/access.2019.2953775

Cited by 25 publications

(16 citation statements)

References 36 publications

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“…If the Red Blood Cell (RBC) and White Blood Cell (WBC) distinction is faulty in the images, the second CNN module performs the classification, and if the HYAL or MUCS discrimination is faulty, the third CNN module performs the classification. 5 To detect only RBCs and WBCs, Zhang et al used the Faster R-CNN-based model. The F1_score of the system, which was able to distinguish between both isomorphic RBC and dysmorphic RBC, was 91.4%.…”

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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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

“…Obviously, CNN is more suitable for classifying U-RBCs than ANN, but the pooling operation in CNN may lose the position features of the images, which will reduce the classification accuracy. To solve this problem, Ji et al [11] were committed to improving the classification indexes of CNN with the support of area feature algorithm (AFA). In summary, although the second research direction contributed a lot of high performance methods, it is really difficult for these methods relying on single focus images to achieve ideal accuracy when classifying the easily deformed U-RBCs.…”

Section: Related Workmentioning

confidence: 99%

“…To solve this problem, Ji et al. [11] were committed to improving the classification indexes of CNN with the support of area feature algorithm (AFA). In summary, although the second research direction contributed a lot of high performance methods, it is really difficult for these methods relying on single focus images to achieve ideal accuracy when classifying the easily deformed U‐RBCs.…”

Section: Related Workmentioning

confidence: 99%

Multi‐instance inflated 3D CNN for classifying urine red blood cells from multi‐focus videos

et al. 2022

IET Image Processing

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Classifying urine red blood cells (U‐RBCs) is the core operation in diagnosing urinary system diseases (USDs). In this paper, based on a novel data type named multi‐focus video, a multi‐instance inflated 3D convolutional neural network (MI3D) is proposed. In order to accurately classifying U‐RBCs, the MI3D integrates inflated inception‐V1 with multi‐instance learning models. Compared with the existent U‐RBC classification methods relying on single focus images, the MI3D using multi‐focus videos effectively avoids the misclassification caused by the significant deformation of U‐RBCs with the focus of microscope changing. In addition, the MI3D can learn the typical shapes and deformation patterns of U‐RBCs from multi‐focal videos simultaneously. Therefore, the accuracy of MI3D exceeds the mainstream video classification models. There are totally 597 multi‐focus videos that include four types of U‐RBCs collected to verify the effectiveness of MI3D. Experimental results show that the classification accuracy of MI3D is inspiring with 94.4%, which is obviously higher than that of existed U‐RBC classification method (85.6%). The accuracy of MI3D also achieves the comparable level with the results by junior microscopist (95.6%). Lastly, the MI3D has powerful real‐time performance, whose classification speed reaches 1.4 times than that of the microscopist.

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“…The process of classifying the particles in the urine is a very complex and difficult process, especially in the presence of a large number of images. In addition, problems are frequently encountered in distinguishing particles of different classes that are similar to each other in manual examinations [ 3 ]. The more accurately the urine sediment particles can be classified, the easier it is for the specialist to diagnose the patient’s disease.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Research on Urine Sediment Images Recognition Based on Deep Learning

Cited by 25 publications

References 36 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

Multi‐instance inflated 3D CNN for classifying urine red blood cells from multi‐focus videos

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

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